The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
This document discusses classifying land cover, forest, and tree species using data from the ZiYuan-3 satellite. It finds that using multiple data sources, like spectral bands, vegetation indices, textures, and topographic factors, improves classification accuracy compared to just using spectral bands. It also finds that combining multi-season images further improves accuracy. While some machine learning algorithms performed better than traditional classifiers when using multiple data sources, no single classification algorithm worked best for all tree species classes. The document concludes it is necessary to develop a customized classification approach using specific data and algorithms for each class.
This document presents a novel approach for detecting and classifying plant leaf diseases using image processing techniques. The proposed framework includes image preprocessing, segmentation of diseased areas using k-means clustering, feature extraction of texture features using GLCM, and classification of diseases using support vector machine (SVM) and decision tree classifiers. The framework is tested on citrus leaf images to detect diseases. Future work could improve classification accuracy and apply the method to other plant species.
The document summarizes a study that mapped forest types and plantation works in Bidar Forest Division, Karnataka, India using remote sensing and GIS techniques. Satellite imagery was analyzed which revealed that 84% of the area is covered by crop land, 1.778% by dry deciduous forest, and 1.38% by mixed plantation. Maps were generated showing forest type distribution and plantation works during 2011-2012 and planned for 2012-2013. Around 12 square kilometers were planted in 2011-2012, with most in Humnbad range and least in Aurad range. Proper forest management can be achieved using geoinformatics technologies.
Comparing Carbon Stock and Increment Estimation using Destructive Sampling an...AI Publications
This research was objectively carried out to compare the forest carbon stock estimation and increment applying destructive sampling and community forest inventory guideline (CFIG). Plantation of Acacia catechu, Dalbergia sissoo, Eucalyptus camaldulensis and Pylunthus emblica at Pragatinagar community forest, Mahottary Nepal was selected for this study. Altogether 62 random points were distributed to select the plant applying stratified random sampling for harvesting purpose. The diameter and height of plants were recorded and these were uprooted. Total 192 discs were slashed each from stem and branches and eight discs cut from the root of plants. The samples were dried in lab. The carbon stock and mean annual carbon increment (MACI) were calculated using both methods to compare the values statistically. The moisture content was about 45.34% in leaves of Eucalyptus camaldulensis and there was about 51 – 58% contribution of stem in total carbon of Pylunthus emblica. Total carbon stock was 3.61±1.32 t/plant of Acacia catechu applying destructive sampling which was 5.37±0.88 t/plant using CFIG and the MACI was 0.72 t/plant of same species using destructive sampling which was 1.07 t/plant using CFIG. The t-test showed significance difference in values of carbon stocks and MACIs at 95% confidence level employing the methods.
A review meta heuristic approaches for solving rectangle packing problemIAEME Publication
This document summarizes various meta-heuristic approaches that have been used to solve the rectangle packing problem, including genetic algorithms, ant colony optimization, particle swarm optimization, iterated local search, and their basic workings. It then reviews literature on applying these meta-heuristics to the rectangular packing problem, describing several approaches that combine genetic algorithms with heuristic packing routines to generate high-quality layouts.
THE USE OF FUZZY SET FUNCTIONS FOR ASSESSING LAND SUITABILITY INDEX AS THE BA...Repository Ipb
This document summarizes a study that used fuzzy set functions in a GIS to evaluate land suitability for different land uses. Membership functions were created for various land attributes based on their suitability for rice, upland crops, and coconut. A joint membership function was determined using the minimum operation. This resulted in continuous land suitability indices from 0 to 1. Maps showed 57% of the area was suitable for rice and upland crops, especially in level to gentle slopes. 68% was suitable for coconut. The fuzzy approach provided a flexible assessment of suitability compared to traditional classification methods.
This document summarizes a study that developed artificial neural network (ANN) models to estimate reference evapotranspiration (ET0) in the Rajendranagar region of Andhra Pradesh, India. Meteorological data from 1978-1993 was used to train and test the ANN models. Multiple and partial correlation analysis identified temperature, wind velocity, sunshine hours, and relative humidity as the main climatic factors influencing ET0 in the region. ANN models with these four inputs and varying numbers of hidden nodes were developed. The optimal ANN model with an architecture of 4 inputs, 3 hidden nodes and 1 output (4-3-1) showed satisfactory performance in estimating daily, weekly, and monthly ET0 based on evaluation metrics
ANNUAL PRECIPITATION IN SOUTHERN OF MADAGASCAR: MODELING USING HIGH ORDER FUZ...ijfls
The objective of this research is to find the best conventional high order fuzzy time series model for annual precipitation series in southern Madagascar. This work consists on finding the hyper parameters (number of partition of the universe of discourse and model order) to obtain the best conventional high
order fuzzy time series model for our experimental data. In previous works, entitled spatial and temporal variability of precipitation in southern Madagascar, we subdivided the study area between 22 ° S to 30 ° S latitude and 43 ° Eto 48 ° E longitude into four zones of homogeneous precipitation. In this article, we seek to model annual precipitation data representative of one of these four areas. These data were taken between 1979 and 2017. Our approach consists on subdividing the data: data obtained from 1979 to 2001 (60%) for the training and data from 2002 to 2017 (40%) to test the model. To determine the number of partitions and model order, we fix first the number of partitions to 10 and then to 15, 20, 25,30, 35, 40, 45 and 50.For each of these values, we vary the model order from 1 to 10.Thenwe locate the model order which corresponds to the minimum of the average curve between the Mean Absolute Errors (MAE) between the training data and the test data. Thus, the orders of the candidate model are 2, 3, 5, and 6.The next step is to fix the model order with the previous values and vary the number of partitions from 3 to 50.For each couple of hyper parameter of the model (number of partitions, model order), we locate the value of number of partitions corresponding to the minimum of the average curve between the absolute mean of the errors or MAE (Mean Absolute Error) between the train and test data. We obtain the hyper-parameter pairs (37, 2), (20, 3), (35, 5) and (35, 6).The first pair gives the lowest Mean Absolute Error. As a final result, we obtain the best high order fuzzy time series model with hyperparameters umber of partition equals thirty seven and of order equals two for annual precipitation in Southern of Madagascar.
This document discusses classifying land cover, forest, and tree species using data from the ZiYuan-3 satellite. It finds that using multiple data sources, like spectral bands, vegetation indices, textures, and topographic factors, improves classification accuracy compared to just using spectral bands. It also finds that combining multi-season images further improves accuracy. While some machine learning algorithms performed better than traditional classifiers when using multiple data sources, no single classification algorithm worked best for all tree species classes. The document concludes it is necessary to develop a customized classification approach using specific data and algorithms for each class.
This document presents a novel approach for detecting and classifying plant leaf diseases using image processing techniques. The proposed framework includes image preprocessing, segmentation of diseased areas using k-means clustering, feature extraction of texture features using GLCM, and classification of diseases using support vector machine (SVM) and decision tree classifiers. The framework is tested on citrus leaf images to detect diseases. Future work could improve classification accuracy and apply the method to other plant species.
The document summarizes a study that mapped forest types and plantation works in Bidar Forest Division, Karnataka, India using remote sensing and GIS techniques. Satellite imagery was analyzed which revealed that 84% of the area is covered by crop land, 1.778% by dry deciduous forest, and 1.38% by mixed plantation. Maps were generated showing forest type distribution and plantation works during 2011-2012 and planned for 2012-2013. Around 12 square kilometers were planted in 2011-2012, with most in Humnbad range and least in Aurad range. Proper forest management can be achieved using geoinformatics technologies.
Comparing Carbon Stock and Increment Estimation using Destructive Sampling an...AI Publications
This research was objectively carried out to compare the forest carbon stock estimation and increment applying destructive sampling and community forest inventory guideline (CFIG). Plantation of Acacia catechu, Dalbergia sissoo, Eucalyptus camaldulensis and Pylunthus emblica at Pragatinagar community forest, Mahottary Nepal was selected for this study. Altogether 62 random points were distributed to select the plant applying stratified random sampling for harvesting purpose. The diameter and height of plants were recorded and these were uprooted. Total 192 discs were slashed each from stem and branches and eight discs cut from the root of plants. The samples were dried in lab. The carbon stock and mean annual carbon increment (MACI) were calculated using both methods to compare the values statistically. The moisture content was about 45.34% in leaves of Eucalyptus camaldulensis and there was about 51 – 58% contribution of stem in total carbon of Pylunthus emblica. Total carbon stock was 3.61±1.32 t/plant of Acacia catechu applying destructive sampling which was 5.37±0.88 t/plant using CFIG and the MACI was 0.72 t/plant of same species using destructive sampling which was 1.07 t/plant using CFIG. The t-test showed significance difference in values of carbon stocks and MACIs at 95% confidence level employing the methods.
A review meta heuristic approaches for solving rectangle packing problemIAEME Publication
This document summarizes various meta-heuristic approaches that have been used to solve the rectangle packing problem, including genetic algorithms, ant colony optimization, particle swarm optimization, iterated local search, and their basic workings. It then reviews literature on applying these meta-heuristics to the rectangular packing problem, describing several approaches that combine genetic algorithms with heuristic packing routines to generate high-quality layouts.
THE USE OF FUZZY SET FUNCTIONS FOR ASSESSING LAND SUITABILITY INDEX AS THE BA...Repository Ipb
This document summarizes a study that used fuzzy set functions in a GIS to evaluate land suitability for different land uses. Membership functions were created for various land attributes based on their suitability for rice, upland crops, and coconut. A joint membership function was determined using the minimum operation. This resulted in continuous land suitability indices from 0 to 1. Maps showed 57% of the area was suitable for rice and upland crops, especially in level to gentle slopes. 68% was suitable for coconut. The fuzzy approach provided a flexible assessment of suitability compared to traditional classification methods.
This document summarizes a study that developed artificial neural network (ANN) models to estimate reference evapotranspiration (ET0) in the Rajendranagar region of Andhra Pradesh, India. Meteorological data from 1978-1993 was used to train and test the ANN models. Multiple and partial correlation analysis identified temperature, wind velocity, sunshine hours, and relative humidity as the main climatic factors influencing ET0 in the region. ANN models with these four inputs and varying numbers of hidden nodes were developed. The optimal ANN model with an architecture of 4 inputs, 3 hidden nodes and 1 output (4-3-1) showed satisfactory performance in estimating daily, weekly, and monthly ET0 based on evaluation metrics
ANNUAL PRECIPITATION IN SOUTHERN OF MADAGASCAR: MODELING USING HIGH ORDER FUZ...ijfls
The objective of this research is to find the best conventional high order fuzzy time series model for annual precipitation series in southern Madagascar. This work consists on finding the hyper parameters (number of partition of the universe of discourse and model order) to obtain the best conventional high
order fuzzy time series model for our experimental data. In previous works, entitled spatial and temporal variability of precipitation in southern Madagascar, we subdivided the study area between 22 ° S to 30 ° S latitude and 43 ° Eto 48 ° E longitude into four zones of homogeneous precipitation. In this article, we seek to model annual precipitation data representative of one of these four areas. These data were taken between 1979 and 2017. Our approach consists on subdividing the data: data obtained from 1979 to 2001 (60%) for the training and data from 2002 to 2017 (40%) to test the model. To determine the number of partitions and model order, we fix first the number of partitions to 10 and then to 15, 20, 25,30, 35, 40, 45 and 50.For each of these values, we vary the model order from 1 to 10.Thenwe locate the model order which corresponds to the minimum of the average curve between the Mean Absolute Errors (MAE) between the training data and the test data. Thus, the orders of the candidate model are 2, 3, 5, and 6.The next step is to fix the model order with the previous values and vary the number of partitions from 3 to 50.For each couple of hyper parameter of the model (number of partitions, model order), we locate the value of number of partitions corresponding to the minimum of the average curve between the absolute mean of the errors or MAE (Mean Absolute Error) between the train and test data. We obtain the hyper-parameter pairs (37, 2), (20, 3), (35, 5) and (35, 6).The first pair gives the lowest Mean Absolute Error. As a final result, we obtain the best high order fuzzy time series model with hyperparameters umber of partition equals thirty seven and of order equals two for annual precipitation in Southern of Madagascar.
Residual stand damages after decreasing on selective cutting diameter limit a...Alexander Decker
- The study examined residual stand damages caused by timber harvesting after decreasing the selective cutting diameter limit at a forest concession in Central Sulawesi, Indonesia.
- Residual stand damage was found to be 23.98%, higher than the previous level of 21.06%. However, both levels are considered light damage (<25%).
- Of the 1,988 damaged trees recorded, 72.30% were heavily damaged, 12.10% were moderately damaged, and 15.60% were lightly damaged.
- Statistically, there was no significant difference in residual stand damage between levels before and after decreasing the diameter limit.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Multiple Crop Classification Using Various Support Vector Machine Kernel Func...IJERA Editor
This study was carried out with techniques of Remote Sensing (RS) based crop discrimination and area estimation with single date approach. Several kernel functions are employed and compared in this study for mapping the input space with including linear, sigmoid, and polynomial and Radial Basis Function (RBF). The present study highlights the advantages of Remote Sensing (RS) and Geographic Information System (GIS) techniques for analyzing the land use/land cover mapping for Aurangabad region of Maharashtra, India. Single date, cloud free IRS-Resourcesat-1 LISS-III data was used for further classification on training set for supervised classification. ENVI 4.4 is used for image analysis and interpretation. The experimental tests show that system is achieved 94.82% using SVM with kernel functions including Polynomial kernel function compared with Radial Basis Function, Sigmoid and linear kernel. The Overall Accuracy (OA) to up to 5.17% in comparison to using sigmoid kernel function, and up to 3.45% in comparison to a 3rd degree polynomial kernel function and RBF with 200 as a penalty parameter.
This document summarizes a study that aimed to identify the best linear time series models to forecast paddy production in Batticaloa District, Sri Lanka. The study analyzed time series data on paddy production from 1980-2013 using various trend and time series models like exponential smoothing, Holt-Winters' method, and ARIMA. The Holt-Winters' method was found to be the best model based on the lowest Mean Absolute Percentage Error and residual analysis. The model forecasted paddy production values of 158695 tons for 2013/14 Maha season, 105481 tons for 2014 Yala season, and 213964 tons for 2014/15 Maha season.
Rice seed image classification based on HOG descriptor with missing values im...TELKOMNIKA JOURNAL
Rice is a primary source of food consumed by almost half of world population. Rice quality mainly depends on the purity of the rice seed. In order to ensure the purity of rice variety, the recognition process is an essential stage. In this paper, we firstly propose to use histogram of oriented gradient (HOG) descriptor to characterize rice seed images. Since the size of image is totally random and the features extracted by HOG can not be used directly by classifier due to the different dimensions. We apply several imputation methods to fill the missing data for HOG descriptor. The experiment is applied on the VNRICE benchmark dataset to evaluate the proposed approach.
The study assessed resource use and technical efficiency in turmeric production in Northern Karnataka. Data was collected from 96 randomly selected turmerc farmers in Bagalkot and Belagavi districts. Analysis found that nearly 50% of farmers had technical efficiency below 90% due to traditional practices. In Belagavi district, planting material, labor, and fertilizers had significant positive impacts on output. In Bagalkot district, animal and machine labor positively impacted output. The study recommends increasing use of certain inputs to improve efficiency and yields.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IRJET- Agricultural Data Modeling and Yield Forecasting using Data Mining...IRJET Journal
This document proposes using data mining techniques to develop a predictive model for forecasting crop yields. It involves collecting agricultural data on factors like rainfall, temperature, seed quality, and sowing procedures. Data preprocessing and clustering techniques like K-means are applied. Classification algorithms like Support Vector Machine and Naive Bayes are used to predict crop yield as low, medium, or high. The predictive model aims to help farmers plan cultivation for high crop yields by identifying the best combinations of agricultural factors.
1) The document discusses predicting soil fertility using machine learning techniques such as decision trees, artificial neural networks, support vector machines, and k-nearest neighbors.
2) It analyzes soil data from Haryana, India to determine the most important properties for defining soil fertility and properties that are highly correlated. Conductivity, water holding capacity, and potassium were found to be most important based on a decision tree analysis.
3) Support vector machines using a radial basis kernel performed best with 80% accuracy compared to 63% for decision trees, 55% for artificial neural networks, and 70% for k-nearest neighbors.
This document describes a study that used remote sensing to classify land use patterns in a region of India. Supervised and unsupervised classification algorithms were applied to a Sentinel-2 satellite image. Maximum likelihood classification achieved the highest overall accuracy of 72.99% among the methods. The classifications were validated using confusion matrices and kappa coefficients. The study aims to help farmers and policymakers with land management and crop production estimates.
A STUDY ON WEED DISCRIMINATION THROUGH WAVELET TRANSFORM, TEXTURE FEATURE EXT...ijcsit
Texture based weed classification has played an important role in agricultural applications. In the recent years weed classification based on wavelet transform is an effective method. But the feature extraction is main issue for proper classification of weed species. In this paper, the issue of statistical and texture
classification based on wavelet transform has been analysed. The efficient texture feature extraction
methods are developed for weed discrimination. Three group feature vector can be constructed by the mean
and standard deviation of the wavelet statistical features (WSF), Texture feature as Contrast, Cluster
Shade, Cluster Prominence and Local Homogeneity (WCSPH) and Energy, Correlation, Cluster Shade,
Cluster Prominence and Entropy features (WECSPE) which are derived from the sub-bands of the wavelet
decomposition and are used for classification. Experimental results show that Rbio33 Wavelet with
WECSPE texture feature obtaining high degree of success rate in classification.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
The document summarizes research on using analytical hierarchy process (AHP) to evaluate environmental factors for determining residential land use suitability. AHP was used to determine weights for various environmental criteria like water availability, flood risk, air pollution, water quality, and distance from waste sites. Spatial data and maps of these factors were analyzed and overlaid based on the AHP weights to produce a final residential land suitability map for the study area of Pimpri-Chinchwad, India. The methodology involved structuring the decision problem hierarchically, making pairwise comparisons to obtain weights, and aggregating weighted criteria maps to determine overall suitability.
Measuring Individual Tree Height and Crown Diameter for Mangrove Trees with A...INFOGAIN PUBLICATION
Mangroves are unique ecosystems that provide valuable coastal area habitats, protection, and services. Access to observing mangrove forests is typically difficult on the ground. Therefore, it is of interest to develop and evaluate remote sensing methods that enable us to obtain accurate information on the structure of mangrove forests and to monitor their condition in time. The main objective of this study was to develop a methodology for processing airborne lidar data for measuring height and crown diameter for mangrove forests in the north-eastern coastal areas of Brazil. Specific objectives were to: (1) evaluate the most appropriate lidar data processing approach, such as area-based or individual tree methods, (2) investigate the most appropriate parameters for lidar-derived data products when estimating height and crown diameter, such as the spatial resolution of canopy height models and ground elevation models; and (3) compare the accuracy of lidar estimates to field measurements of height and crown diameter. The lidar dataset was acquired over mangrove forest of the northeast of Brazil. The crown diameter was calculated as the average of two values measured along two perpendicular directions from the location of each tree top by fitting a fourth-degree polynomial on both profiles. The lidar-derived tree measurements were used with regression models and cross-validation to estimate plot level field-measured crown diameter. Root mean square error, linear regression and the Nash-Sutcliffe coefficient were also used to compare lidar height and field height. The mean of lidar-estimated tree height was 9,48m and the mean of field tree height was 8.44m. The correlation between lidar tree height and field tree height was r= 0.60, E=-0.06 and RMSE= 2.8. The correlation between height and crown diameter needed to parameterized the individual tree identification software obtained for 32 trees was r= 0.83 and determination coefficient was r2 = 0.69. The results of the current study show that lidar data could be used to estimate height and average crown diameter of mangrove trees and to improve estimates of other mangrove forest biophysical parameters of interest by focusing at the individual tree level. The research presented in this study contributes to the overall knowledge of using lidar remote sensing to measure and monitor mangrove forests.
Measuring Individual Tree Height and Crown Diameter for Mangrove Trees with A...INFOGAIN PUBLICATION
Mangroves are unique ecosystems that provide valuable coastal area habitats, protection, and services. Access to observing mangrove forests is typically difficult on the ground. Therefore, it is of interest to develop and evaluate remote sensing methods that enable us to obtain accurate information on the structure of mangrove forests and to monitor their condition in time. The main objective of this study was to develop a methodology for processing airborne lidar data for measuring height and crown diameter for mangrove forests in the north-eastern coastal areas of Brazil. Specific objectives were to: (1) evaluate the most appropriate lidar data processing approach, such as area-based or individual tree methods, (2) investigate the most appropriate parameters for lidar-derived data products when estimating height and crown diameter, such as the spatial resolution of canopy height models and ground elevation models; and (3) compare the accuracy of lidar estimates to field measurements of height and crown diameter. The lidar dataset was acquired over mangrove forest of the northeast of Brazil. The crown diameter was calculated as the average of two values measured along two perpendicular directions from the location of each tree top by fitting a fourth-degree polynomial on both profiles. The lidar-derived tree measurements were used with regression models and cross-validation to estimate plot level field-measured crown diameter. Root mean square error, linear regression and the Nash-Sutcliffe coefficient were also used to compare lidar height and field height. The mean of lidar-estimated tree height was 9,48m and the mean of field tree height was 8.44m. The correlation between lidar tree height and field tree height was r= 0.60, E=-0.06 and RMSE= 2.8. The correlation between height and crown diameter needed to parameterized the individual tree identification software obtained for 32 trees was r= 0.83 and determination coefficient was r2 = 0.69. The results of the current study show that lidar data could be used to estimate height and average crown diameter of mangrove trees and to improve estimates of other mangrove forest biophysical parameters of interest by focusing at the individual tree level. The research presented in this study contributes to the overall knowledge of using lidar remote sensing to measure and monitor mangrove forests.
Measuring Individual Tree Height and Crown Diameter for Mangrove Trees with A...INFOGAIN PUBLICATION
Mangroves are unique ecosystems that provide valuable coastal area habitats, protection, and services. Access to observing mangrove forests is typically difficult on the ground. Therefore, it is of interest to develop and evaluate remote sensing methods that enable us to obtain accurate information on the structure of mangrove forests and to monitor their condition in time. The main objective of this study was to develop a methodology for processing airborne lidar data for measuring height and crown diameter for mangrove forests in the north-eastern coastal areas of Brazil. Specific objectives were to: (1) evaluate the most appropriate lidar data processing approach, such as area-based or individual tree methods, (2) investigate the most appropriate parameters for lidar-derived data products when estimating height and crown diameter, such as the spatial resolution of canopy height models and ground elevation models; and (3) compare the accuracy of lidar estimates to field measurements of height and crown diameter. The lidar dataset was acquired over mangrove forest of the northeast of Brazil. The crown diameter was calculated as the average of two values measured along two perpendicular directions from the location of each tree top by fitting a fourth-degree polynomial on both profiles. The lidar-derived tree measurements were used with regression models and cross-validation to estimate plot level field-measured crown diameter. Root mean square error, linear regression and the Nash-Sutcliffe coefficient were also used to compare lidar height and field height. The mean of lidar-estimated tree height was 9,48m and the mean of field tree height was 8.44m. The correlation between lidar tree height and field tree height was r= 0.60, E=-0.06 and RMSE= 2.8. The correlation between height and crown diameter needed to parameterized the individual tree identification software obtained for 32 trees was r= 0.83 and determination coefficient was r2 = 0.69. The results of the current study show that lidar data could be used to estimate height and average crown diameter of mangrove trees and to improve estimates of other mangrove forest biophysical parameters of interest by focusing at the individual tree level. The research presented in this study contributes to the overall knowledge of using lidar remote sensing to measure and monitor mangrove forests.
NIR in Leaf Chlorophyll Concentration EstimationIRJET Journal
This document discusses using field imaging spectroscopy and random forests to estimate leaf chlorophyll concentration in soybeans. A field imaging spectrometer system was used to collect hyperspectral reflectance data from soybean leaves. The PROSPECT radiative transfer model was used to relate the spectral data to chlorophyll content. Random forests were then used to develop a model for estimating chlorophyll content based on the spectral data. The model was able to accurately retrieve chlorophyll content from validation spectral data, demonstrating the potential of this approach for remote monitoring of crop health conditions. Future work involves applying this method from field to satellite data to allow monitoring soybean health over large areas.
IRJET- Optimum Band Selection in Sentinel-2A Satellite for Crop Classificatio...IRJET Journal
This document discusses using machine learning techniques and Sentinel-2A satellite data for crop classification. Specifically, it examines selecting the optimal bands from Sentinel-2A for crop classification. Sentinel-2A has 13 spectral bands at various resolutions that provide valuable data for vegetation mapping and crop classification when used with machine learning algorithms like random forests and support vector machines. Previous studies have effectively used indices calculated from Sentinel-2A bands as well as directly using selected bands for crop classification, finding that using selected bands can improve classification performance over using indices alone. The goal of this study is to evaluate Sentinel-2A data capabilities for crop type classification by implementing AI algorithms.
Assessment of wheat crop coefficient using remote sensing techniquesPremier Publishers
Irrigation water consumption under physical and climatic conditions for large scale will be easier with remote sensing techniques. Crop evapotranspiration (ETc) uses crop coefficient (Kc) and reference evapotranspiration (ETo). Kc plays an essential role in agricultural practices and it has been widely used to estimate ETc. In this paper Normalized Deference Vegetation Index (NDVI) used to estimate crop coefficient according to satellite data (KcSat) through simple model (KcSat = 2NDVI - 0.2). Landsat8; bands 4 and 5 provide Red (R) and Near Infra-Red (NIR) measurements and it used to calculate NDVI. Single KcFAO estimated under Egyptian conditions according to FAO 56 paper. The KcFAO used to validate KcSat. Linear relationship between KcFAO and KcSat was established and R2 was 0.96. The main objective of this paper is estimation of wheat crop coefficient using remote sensing techniques.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Lake sediment thickness estimation using ground penetrating radareSAT Publishing House
This document summarizes a study that used ground penetrating radar (GPR) to estimate the thickness and volume of sediments in Punem Lake, India. GPR profiles identified two sediment layers in the lake. The thickness of the first layer ranged from 0.02m to 1.16m with an average of 0.5m, while the second layer ranged from 0.08m to 0.99m with an average of 0.37m. Sediment volume was estimated at 260,303 cubic meters for the first layer and 188,171 cubic meters for the second layer. Thicker sediments were found toward the north, northeast, and east of the lake for the first layer and toward the north, northeast, and west for
Evaporation and Production Efficiency Modelling Using Fuzzy Linear RecurrenceAI Publications
The relationship between crop production and amount of evapotranspiration is very important to agronomists, engineers, economists, and water resources planners. These relationships are often determined using classical least square regression (LSR). However, one needs high amount of samples to determine probability distribution function. Linear regression also requires so many measurements to obtain the valid estimates of crop production function coefficients. In addition, deriving ET-yield regression for each crop and each district is usually expensive, since lysimetric experiments should be repeated for several years for each crop. The object of this study is to introduce a fuzzy linear regression as an alternative approach to statistical regression analysis in determining coefficients of ET- yield relations for each crop and each district with minimum data. The application of possibilistic regression has been examined with a case study. Two data set for winter wheat in Loss Plateau of China and North China Plain have been used. The current finding shows capability of possibilistic regression in estimation of crop yield in data shortage conditions.
On the performance analysis of rainfall prediction using mutual information...IJECEIAES
Monsoon rainfall prediction over a small geographic region is indeed a challenging task. This paper uses monthly means of climate variables, namely air temperature (AT), sea surface temperature (SST), and sea level pressure (SLP) over the globe, to predict monthly and seasonal summer monsoon rainfall over the state of Maharashtra, India. Mutual information correlates the temperature and pressure from a grid of 10 longitude X 10 latitude with Maharashtra’s monthly rainfall time series. Based on the correlations, selected features over the respective latitude and longitudes are given as inputs to an artificial neural network. It was observed that AT and SLP could predict monthly monsoon rainfall with excellent accuracy. The performance of the test dataset was evaluated through mean absolute error; root mean square error, correlation coefficient, Nash Sutcliffe model efficiency coefficient, and maximum rainfall prediction capability of the network. The individual climate variable model for AT performed better in all evaluation parameters except maximum rainfall capability, where the combined model 2 with AT, SLP and SST as predictors outperformed. The SLP-only model’s performance was comparable to the AT-only model. The combined model 1 with AT and SLP as predictors was found better than the combined model 2.
Residual stand damages after decreasing on selective cutting diameter limit a...Alexander Decker
- The study examined residual stand damages caused by timber harvesting after decreasing the selective cutting diameter limit at a forest concession in Central Sulawesi, Indonesia.
- Residual stand damage was found to be 23.98%, higher than the previous level of 21.06%. However, both levels are considered light damage (<25%).
- Of the 1,988 damaged trees recorded, 72.30% were heavily damaged, 12.10% were moderately damaged, and 15.60% were lightly damaged.
- Statistically, there was no significant difference in residual stand damage between levels before and after decreasing the diameter limit.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Multiple Crop Classification Using Various Support Vector Machine Kernel Func...IJERA Editor
This study was carried out with techniques of Remote Sensing (RS) based crop discrimination and area estimation with single date approach. Several kernel functions are employed and compared in this study for mapping the input space with including linear, sigmoid, and polynomial and Radial Basis Function (RBF). The present study highlights the advantages of Remote Sensing (RS) and Geographic Information System (GIS) techniques for analyzing the land use/land cover mapping for Aurangabad region of Maharashtra, India. Single date, cloud free IRS-Resourcesat-1 LISS-III data was used for further classification on training set for supervised classification. ENVI 4.4 is used for image analysis and interpretation. The experimental tests show that system is achieved 94.82% using SVM with kernel functions including Polynomial kernel function compared with Radial Basis Function, Sigmoid and linear kernel. The Overall Accuracy (OA) to up to 5.17% in comparison to using sigmoid kernel function, and up to 3.45% in comparison to a 3rd degree polynomial kernel function and RBF with 200 as a penalty parameter.
This document summarizes a study that aimed to identify the best linear time series models to forecast paddy production in Batticaloa District, Sri Lanka. The study analyzed time series data on paddy production from 1980-2013 using various trend and time series models like exponential smoothing, Holt-Winters' method, and ARIMA. The Holt-Winters' method was found to be the best model based on the lowest Mean Absolute Percentage Error and residual analysis. The model forecasted paddy production values of 158695 tons for 2013/14 Maha season, 105481 tons for 2014 Yala season, and 213964 tons for 2014/15 Maha season.
Rice seed image classification based on HOG descriptor with missing values im...TELKOMNIKA JOURNAL
Rice is a primary source of food consumed by almost half of world population. Rice quality mainly depends on the purity of the rice seed. In order to ensure the purity of rice variety, the recognition process is an essential stage. In this paper, we firstly propose to use histogram of oriented gradient (HOG) descriptor to characterize rice seed images. Since the size of image is totally random and the features extracted by HOG can not be used directly by classifier due to the different dimensions. We apply several imputation methods to fill the missing data for HOG descriptor. The experiment is applied on the VNRICE benchmark dataset to evaluate the proposed approach.
The study assessed resource use and technical efficiency in turmeric production in Northern Karnataka. Data was collected from 96 randomly selected turmerc farmers in Bagalkot and Belagavi districts. Analysis found that nearly 50% of farmers had technical efficiency below 90% due to traditional practices. In Belagavi district, planting material, labor, and fertilizers had significant positive impacts on output. In Bagalkot district, animal and machine labor positively impacted output. The study recommends increasing use of certain inputs to improve efficiency and yields.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IRJET- Agricultural Data Modeling and Yield Forecasting using Data Mining...IRJET Journal
This document proposes using data mining techniques to develop a predictive model for forecasting crop yields. It involves collecting agricultural data on factors like rainfall, temperature, seed quality, and sowing procedures. Data preprocessing and clustering techniques like K-means are applied. Classification algorithms like Support Vector Machine and Naive Bayes are used to predict crop yield as low, medium, or high. The predictive model aims to help farmers plan cultivation for high crop yields by identifying the best combinations of agricultural factors.
1) The document discusses predicting soil fertility using machine learning techniques such as decision trees, artificial neural networks, support vector machines, and k-nearest neighbors.
2) It analyzes soil data from Haryana, India to determine the most important properties for defining soil fertility and properties that are highly correlated. Conductivity, water holding capacity, and potassium were found to be most important based on a decision tree analysis.
3) Support vector machines using a radial basis kernel performed best with 80% accuracy compared to 63% for decision trees, 55% for artificial neural networks, and 70% for k-nearest neighbors.
This document describes a study that used remote sensing to classify land use patterns in a region of India. Supervised and unsupervised classification algorithms were applied to a Sentinel-2 satellite image. Maximum likelihood classification achieved the highest overall accuracy of 72.99% among the methods. The classifications were validated using confusion matrices and kappa coefficients. The study aims to help farmers and policymakers with land management and crop production estimates.
A STUDY ON WEED DISCRIMINATION THROUGH WAVELET TRANSFORM, TEXTURE FEATURE EXT...ijcsit
Texture based weed classification has played an important role in agricultural applications. In the recent years weed classification based on wavelet transform is an effective method. But the feature extraction is main issue for proper classification of weed species. In this paper, the issue of statistical and texture
classification based on wavelet transform has been analysed. The efficient texture feature extraction
methods are developed for weed discrimination. Three group feature vector can be constructed by the mean
and standard deviation of the wavelet statistical features (WSF), Texture feature as Contrast, Cluster
Shade, Cluster Prominence and Local Homogeneity (WCSPH) and Energy, Correlation, Cluster Shade,
Cluster Prominence and Entropy features (WECSPE) which are derived from the sub-bands of the wavelet
decomposition and are used for classification. Experimental results show that Rbio33 Wavelet with
WECSPE texture feature obtaining high degree of success rate in classification.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
The document summarizes research on using analytical hierarchy process (AHP) to evaluate environmental factors for determining residential land use suitability. AHP was used to determine weights for various environmental criteria like water availability, flood risk, air pollution, water quality, and distance from waste sites. Spatial data and maps of these factors were analyzed and overlaid based on the AHP weights to produce a final residential land suitability map for the study area of Pimpri-Chinchwad, India. The methodology involved structuring the decision problem hierarchically, making pairwise comparisons to obtain weights, and aggregating weighted criteria maps to determine overall suitability.
Measuring Individual Tree Height and Crown Diameter for Mangrove Trees with A...INFOGAIN PUBLICATION
Mangroves are unique ecosystems that provide valuable coastal area habitats, protection, and services. Access to observing mangrove forests is typically difficult on the ground. Therefore, it is of interest to develop and evaluate remote sensing methods that enable us to obtain accurate information on the structure of mangrove forests and to monitor their condition in time. The main objective of this study was to develop a methodology for processing airborne lidar data for measuring height and crown diameter for mangrove forests in the north-eastern coastal areas of Brazil. Specific objectives were to: (1) evaluate the most appropriate lidar data processing approach, such as area-based or individual tree methods, (2) investigate the most appropriate parameters for lidar-derived data products when estimating height and crown diameter, such as the spatial resolution of canopy height models and ground elevation models; and (3) compare the accuracy of lidar estimates to field measurements of height and crown diameter. The lidar dataset was acquired over mangrove forest of the northeast of Brazil. The crown diameter was calculated as the average of two values measured along two perpendicular directions from the location of each tree top by fitting a fourth-degree polynomial on both profiles. The lidar-derived tree measurements were used with regression models and cross-validation to estimate plot level field-measured crown diameter. Root mean square error, linear regression and the Nash-Sutcliffe coefficient were also used to compare lidar height and field height. The mean of lidar-estimated tree height was 9,48m and the mean of field tree height was 8.44m. The correlation between lidar tree height and field tree height was r= 0.60, E=-0.06 and RMSE= 2.8. The correlation between height and crown diameter needed to parameterized the individual tree identification software obtained for 32 trees was r= 0.83 and determination coefficient was r2 = 0.69. The results of the current study show that lidar data could be used to estimate height and average crown diameter of mangrove trees and to improve estimates of other mangrove forest biophysical parameters of interest by focusing at the individual tree level. The research presented in this study contributes to the overall knowledge of using lidar remote sensing to measure and monitor mangrove forests.
Measuring Individual Tree Height and Crown Diameter for Mangrove Trees with A...INFOGAIN PUBLICATION
Mangroves are unique ecosystems that provide valuable coastal area habitats, protection, and services. Access to observing mangrove forests is typically difficult on the ground. Therefore, it is of interest to develop and evaluate remote sensing methods that enable us to obtain accurate information on the structure of mangrove forests and to monitor their condition in time. The main objective of this study was to develop a methodology for processing airborne lidar data for measuring height and crown diameter for mangrove forests in the north-eastern coastal areas of Brazil. Specific objectives were to: (1) evaluate the most appropriate lidar data processing approach, such as area-based or individual tree methods, (2) investigate the most appropriate parameters for lidar-derived data products when estimating height and crown diameter, such as the spatial resolution of canopy height models and ground elevation models; and (3) compare the accuracy of lidar estimates to field measurements of height and crown diameter. The lidar dataset was acquired over mangrove forest of the northeast of Brazil. The crown diameter was calculated as the average of two values measured along two perpendicular directions from the location of each tree top by fitting a fourth-degree polynomial on both profiles. The lidar-derived tree measurements were used with regression models and cross-validation to estimate plot level field-measured crown diameter. Root mean square error, linear regression and the Nash-Sutcliffe coefficient were also used to compare lidar height and field height. The mean of lidar-estimated tree height was 9,48m and the mean of field tree height was 8.44m. The correlation between lidar tree height and field tree height was r= 0.60, E=-0.06 and RMSE= 2.8. The correlation between height and crown diameter needed to parameterized the individual tree identification software obtained for 32 trees was r= 0.83 and determination coefficient was r2 = 0.69. The results of the current study show that lidar data could be used to estimate height and average crown diameter of mangrove trees and to improve estimates of other mangrove forest biophysical parameters of interest by focusing at the individual tree level. The research presented in this study contributes to the overall knowledge of using lidar remote sensing to measure and monitor mangrove forests.
Measuring Individual Tree Height and Crown Diameter for Mangrove Trees with A...INFOGAIN PUBLICATION
Mangroves are unique ecosystems that provide valuable coastal area habitats, protection, and services. Access to observing mangrove forests is typically difficult on the ground. Therefore, it is of interest to develop and evaluate remote sensing methods that enable us to obtain accurate information on the structure of mangrove forests and to monitor their condition in time. The main objective of this study was to develop a methodology for processing airborne lidar data for measuring height and crown diameter for mangrove forests in the north-eastern coastal areas of Brazil. Specific objectives were to: (1) evaluate the most appropriate lidar data processing approach, such as area-based or individual tree methods, (2) investigate the most appropriate parameters for lidar-derived data products when estimating height and crown diameter, such as the spatial resolution of canopy height models and ground elevation models; and (3) compare the accuracy of lidar estimates to field measurements of height and crown diameter. The lidar dataset was acquired over mangrove forest of the northeast of Brazil. The crown diameter was calculated as the average of two values measured along two perpendicular directions from the location of each tree top by fitting a fourth-degree polynomial on both profiles. The lidar-derived tree measurements were used with regression models and cross-validation to estimate plot level field-measured crown diameter. Root mean square error, linear regression and the Nash-Sutcliffe coefficient were also used to compare lidar height and field height. The mean of lidar-estimated tree height was 9,48m and the mean of field tree height was 8.44m. The correlation between lidar tree height and field tree height was r= 0.60, E=-0.06 and RMSE= 2.8. The correlation between height and crown diameter needed to parameterized the individual tree identification software obtained for 32 trees was r= 0.83 and determination coefficient was r2 = 0.69. The results of the current study show that lidar data could be used to estimate height and average crown diameter of mangrove trees and to improve estimates of other mangrove forest biophysical parameters of interest by focusing at the individual tree level. The research presented in this study contributes to the overall knowledge of using lidar remote sensing to measure and monitor mangrove forests.
NIR in Leaf Chlorophyll Concentration EstimationIRJET Journal
This document discusses using field imaging spectroscopy and random forests to estimate leaf chlorophyll concentration in soybeans. A field imaging spectrometer system was used to collect hyperspectral reflectance data from soybean leaves. The PROSPECT radiative transfer model was used to relate the spectral data to chlorophyll content. Random forests were then used to develop a model for estimating chlorophyll content based on the spectral data. The model was able to accurately retrieve chlorophyll content from validation spectral data, demonstrating the potential of this approach for remote monitoring of crop health conditions. Future work involves applying this method from field to satellite data to allow monitoring soybean health over large areas.
IRJET- Optimum Band Selection in Sentinel-2A Satellite for Crop Classificatio...IRJET Journal
This document discusses using machine learning techniques and Sentinel-2A satellite data for crop classification. Specifically, it examines selecting the optimal bands from Sentinel-2A for crop classification. Sentinel-2A has 13 spectral bands at various resolutions that provide valuable data for vegetation mapping and crop classification when used with machine learning algorithms like random forests and support vector machines. Previous studies have effectively used indices calculated from Sentinel-2A bands as well as directly using selected bands for crop classification, finding that using selected bands can improve classification performance over using indices alone. The goal of this study is to evaluate Sentinel-2A data capabilities for crop type classification by implementing AI algorithms.
Assessment of wheat crop coefficient using remote sensing techniquesPremier Publishers
Irrigation water consumption under physical and climatic conditions for large scale will be easier with remote sensing techniques. Crop evapotranspiration (ETc) uses crop coefficient (Kc) and reference evapotranspiration (ETo). Kc plays an essential role in agricultural practices and it has been widely used to estimate ETc. In this paper Normalized Deference Vegetation Index (NDVI) used to estimate crop coefficient according to satellite data (KcSat) through simple model (KcSat = 2NDVI - 0.2). Landsat8; bands 4 and 5 provide Red (R) and Near Infra-Red (NIR) measurements and it used to calculate NDVI. Single KcFAO estimated under Egyptian conditions according to FAO 56 paper. The KcFAO used to validate KcSat. Linear relationship between KcFAO and KcSat was established and R2 was 0.96. The main objective of this paper is estimation of wheat crop coefficient using remote sensing techniques.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Lake sediment thickness estimation using ground penetrating radareSAT Publishing House
This document summarizes a study that used ground penetrating radar (GPR) to estimate the thickness and volume of sediments in Punem Lake, India. GPR profiles identified two sediment layers in the lake. The thickness of the first layer ranged from 0.02m to 1.16m with an average of 0.5m, while the second layer ranged from 0.08m to 0.99m with an average of 0.37m. Sediment volume was estimated at 260,303 cubic meters for the first layer and 188,171 cubic meters for the second layer. Thicker sediments were found toward the north, northeast, and east of the lake for the first layer and toward the north, northeast, and west for
Evaporation and Production Efficiency Modelling Using Fuzzy Linear RecurrenceAI Publications
The relationship between crop production and amount of evapotranspiration is very important to agronomists, engineers, economists, and water resources planners. These relationships are often determined using classical least square regression (LSR). However, one needs high amount of samples to determine probability distribution function. Linear regression also requires so many measurements to obtain the valid estimates of crop production function coefficients. In addition, deriving ET-yield regression for each crop and each district is usually expensive, since lysimetric experiments should be repeated for several years for each crop. The object of this study is to introduce a fuzzy linear regression as an alternative approach to statistical regression analysis in determining coefficients of ET- yield relations for each crop and each district with minimum data. The application of possibilistic regression has been examined with a case study. Two data set for winter wheat in Loss Plateau of China and North China Plain have been used. The current finding shows capability of possibilistic regression in estimation of crop yield in data shortage conditions.
On the performance analysis of rainfall prediction using mutual information...IJECEIAES
Monsoon rainfall prediction over a small geographic region is indeed a challenging task. This paper uses monthly means of climate variables, namely air temperature (AT), sea surface temperature (SST), and sea level pressure (SLP) over the globe, to predict monthly and seasonal summer monsoon rainfall over the state of Maharashtra, India. Mutual information correlates the temperature and pressure from a grid of 10 longitude X 10 latitude with Maharashtra’s monthly rainfall time series. Based on the correlations, selected features over the respective latitude and longitudes are given as inputs to an artificial neural network. It was observed that AT and SLP could predict monthly monsoon rainfall with excellent accuracy. The performance of the test dataset was evaluated through mean absolute error; root mean square error, correlation coefficient, Nash Sutcliffe model efficiency coefficient, and maximum rainfall prediction capability of the network. The individual climate variable model for AT performed better in all evaluation parameters except maximum rainfall capability, where the combined model 2 with AT, SLP and SST as predictors outperformed. The SLP-only model’s performance was comparable to the AT-only model. The combined model 1 with AT and SLP as predictors was found better than the combined model 2.
An Ear Recognition Method Based on Rotation Invariant Transformed DCT IJECEIAES
Human recognition systems have gained great importance recently in a wide range of applications like access, control, criminal investigation and border security. Ear is an emerging biometric which has rich and stable structure and can potentially be implemented reliably and cost efficiently. Thus human ear recognition has been researched widely and made greatly progress. High recognition rates which are reported in most existing methods can be reached only under closely controlled conditions. Actually a slight amount of rotation and translation which is inescapable would be injurious for system performance. In this paper, a method that uses a transformed type of DCT is implemented to extract meaningful features from ear images. This algorithm is quite robust to ear rotation, translation and illumination. The proposed method is experimented on two popular databases, i.e. USTB II and IIT Delhi II, which achieves significant improvement in the performance in comparison to other methods with good efficiency based on LBP, DSIFT and Gabor. Also because of considering only important coefficients, this method is faster compared to other methods.
Analysis of crop yield prediction using data mining techniqueseSAT Journals
Abstract
Agrarian sector in India is facing rigorous problem to maximize the crop productivity. More than 60 percent of the crop still depends on monsoon rainfall. Recent developments in Information Technology for agriculture field has become an interesting research area to predict the crop yield. The problem of yield prediction is a major problem that remains to be solved based on available data. Data Mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. This paper presents a brief analysis of crop yield prediction using Multiple Linear Regression (MLR) technique and Density based clustering technique for the selected region i.e. East Godavari district of Andhra Pradesh in India.
Keywords: Agrarian Sector, Crop Production, Data Mining, Density based clustering, Information Technology, Multiple Linear Regression, Yield Prediction.
Crop yield prediction using ridge regression.pdfssuserb22f5a
Crop yield prediction using deep neural networks with data mining concepts by applying multi model ensembles using ridge regression to increase accuracy, precision, recall,and f measure. Combining neural networks with regression increase high satisfactory crop yield prediction.the support vector regression is slow convergence , stuck in local minima. But ridge regression analyse multicollinearity in multiple regression.
IMAGE PROCESSING AND CLUSTERING ALGORITHMS FOR FOREST COVER QUANTIFICATIONIAEME Publication
“Forest cover” refers to the relative land area covered by forests. Anthropological interventions and the subsequent diminishing forest cover, result in environmental degradation, impacting man-nature interactions. Hence, it became the need of the moment to monitor the forest cover to minimize natural perils and promote sustainable development. The present preliminary work focuses on implementing image processing and k- means clustering techniques on satellite imagery to monitor and quantify the forest cover of the Sundarbans delta, existing across India and Bangladesh. Imagebased algorithms relying on characteristic colouration were proposed for analysing the percentage of forest cover in the predefined area. Among various methods of monitoring and examining forest land, image-based algorithms can be of vital use due to the rise in the accessibility of information and the potential of analysing large data sets with the least processing time. The above-discussed techniques, along with the availability of Machine Learning (ML) and spaceborne photography, will have a futuristic impact on interpreting the variations in land cover and land utilization. Building upon the following algorithm, it is now conceivable to conduct timely comprehensive analysis, real-time evaluation, monitoring, and control on how events unfold. Similarly, data collected from various geographical observation systems may provide several other qualitative features that are more focused.
Assessing the performance of random forest regression for estimating canopy h...IJECEIAES
Accurate estimation of forest canopy height is essential for monitoring forest ecosystems and assessing their carbon storage potential. This study evaluates the effectiveness of different remote sensing techniques for estimating forest canopy height in tropical dry forests. Using field data and remote sensing data from airborne lidar and polarimetric synthetic aperture radar (SAR), a random forest (RF) model was developed to estimate canopy height based on different indices. Results show that the normalize difference build-up index (NDBI) has the highest correlation with canopy height, outperforming other indices such as relative vigor index (RVI) and polarimetric vertical and horizontal variables. The RF model with NDBI as input showed a good fit and predictive ability, with low concentration of errors around 0. These findings suggest that NDBI can be a useful tool for accurately estimating forest canopy height in tropical dry forests using remote sensing techniques, providing valuable information for forest management and conservation efforts.
Carbon Stocks Estimation in South East Sulawesi Tropical Forest, Indonesia, u...ijceronline
This document summarizes a study that estimated carbon stocks in a tropical forest in Southeast Sulawesi, Indonesia using Polarimetric Interferometry Synthetic Aperture Radar (PolInSAR). Two ALOS PALSAR images with a 46 day temporal baseline were processed to form a Random Volume over Ground model and optimize coherence. Field measurements of tree heights and diameters were converted to carbon stock volumes using allometric equations. The resulting forest heights and carbon stocks from PolInSAR inversion correlated positively with ground measurements.
Development of Adaptive Neuro Fuzzy Inference System for Estimation of Evapot...ijsrd.com
The accuracy of an adaptive neurofuzzy computing technique in estimation of reference evapotranspiration (ETo) is investigated in this paper. The model is based on Adaptive Neurofuzzy Inference System (ANFIS) and uses commonly available weather information such as the daily climatic data, Maximum and Minimum Air Temperature, Relative Humidity, Wind Speed and Sunshine hours from station, Karjan (Latitude - 22°03'10.95"N, Longitude - 73°07'24.65"E), in Vadodara (Gujarat), are used as inputs to the neurofuzzy model to estimate ETo obtained using the FAO-56 Penman.Monteith equation. The daily meteorological data of two years from 2009 and 2010 at Karjan Takuka, Vadodara, are used to train the model, and the data in 2011 is used to predict the ETo in that year and to validate the model. The ETo in training period (Train- ETo) and the predicted results (Test-ETo) are compared with the ETo computed by Penman-Monteith method (PM-ETo) using "gDailyET" Software. The results indicate that the PM-ETo values are closely and linearly correlated with Train- ETo and Test- ETo with Root Mean Squared Error (RMSE) and showed the higher significances of the Train- ETo and Test- ETo. The results indict the feasibility of using the convenient model to resolve the problems of agriculture irrigation with intelligent algorithm, and more accurate weather forecast, appropriate membership function and suitable fuzzy rules.
This document provides instructions for using a toolbox to run general additive models (GAMs) to model habitat for three fish species using data from NOAA trawl surveys. The toolbox allows the user to: 1) extract data from trawl surveys, 2) fit GAMs and select important environmental variables, and 3) predict probabilities and create habitat rasters. Key steps include extracting presence/absence data, running initial GAMs with all variables to select important ones, refitting GAMs with the selected variables, and using the results to create probability and habitat rasters. The overall goal is to objectively model species distributions based on environmental parameters.
Landuse landcover and ndvi analysis for halia catchmentIAEME Publication
This document summarizes a study analyzing land use/land cover changes and normalized difference vegetation index (NDVI) for the Halia catchment area in India over several decades using remote sensing data. Medium to high resolution Landsat satellite imagery from 1975, 1989, and 2001 was processed to create land use/land cover maps and NDVI maps for the area. The objective was to examine changes in cropped area and land use/land cover patterns over time and understand the implications for the local environment.
Estimation of land surface temperature of dindigul district using landsat 8 dataeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Similar to The International Journal of Engineering and Science (The IJES) (20)
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
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The International Journal of Engineering and Science (The IJES)
1. The International Journal Of Engineering And Science (IJES)
||Volume|| 2 ||Issue|| 11 ||Pages|| 118-123 || 2013 ||
ISSN (e): 2319 – 1813 ISSN (p): 2319 – 1805
On The Use of an Almost Unbiased Ratio Estimator in the TwoPhase Sampling Scheme
Olusegun Alfred O.
--------------------------------------------------------- ABSTRACT-----------------------------------------------------------There has been an extensive use of auxiliary information in ratio estimation for estimating population
parameters in forestry. Tree heights, measured during forest inventory programs is an important component
required for determining a forest site’s productivity. We consider the problem of obtaining precise estimates of
the mean height of trees in a finite population. Using simple random sampling without replacement, data on the
total height of Tectona grandis (THT), the study variate and its diameter-at-breast height (DBH), which is the
auxiliary information were obtained via the two-phase sampling approach at the Oluwa Forest Reserve, Ondo
State, Nigeria. The estimators considered in this study are the unbiased estimator, the usual ratio estimator, the
Bahl and Tuteja (1991) estimator and the almost unbiased (AU) estimator. The Bias and the mean square error
(MSE) of these estimators were then obtained to the first degree of approximation, using the Taylor’s
linearization method as described by Wolter (2007). The inequality which expresses the relationship obtained
from a direct comparison of the MSE of estimators, an observation of findings,indicate that AU ratio estimator
has the highest percent relative gain in efficiency.
KEYWORDS: Auxiliary Information,Mean Square Error, Ratio Estimator, Tectona grandis, Two-Phase
Sampling
------------------------------------------------------------------------------------------------------- --------------------------------Date of Submission: 11, September - 2013
Date of Acceptance: 10, November - 2013
----------------------------------------------------------------------------------------------------------- -----------------------------
I. INTRODUCTION
Demand of statistics on various facets of economy in all the countries is increasing at a fast rate. In
developing countries, basic data are needed for formulating development plans and for subsequent assessment of
their progress. For this purpose, it becomes necessary to collect relevant information regarding the different
sectors of economy for each of the administrative divisions. With respect to wood and timber in the forestry and
paper industry, data on various aspects of its cultivation are needed for planning, fixing the target of production
and for assessing the progress of various development programmes being conducted in the sector. Census,
which is a complicated and costly operation, cannot be operated on a very frequent interval in order to obtain the
needed information. Hence, this necessity, which has led in part, to this research work, the considerable
development in the applications of efficient and modern sampling techniques based on probability theory.
Teak trees with botanical name Tectona grandis is a genus of tropical hardwood trees in the family
verbenaceae native to south and south-east of Asia, the timber from teak is used in shipbuilding, boat decks and
in the manufacture of outdoor furniture and other articles where weather resistance is desired. In south-western
Nigeria, Tectona grandis is one of the most prominent species in the man-made forests. It is regarded as a very
suitable specie for the rapid production of large volumes of timber, fuel-wood and poles of uniform and
desirable quality (Akindele 1991), e.g. PHCN poles. Teak is also used extensively to make doors and window
frames as it is resistant to termite attack Oyamakin et al (2010).
The concept of double sampling was first introduced by Neyman (1938). He felt the need of double
sampling technique while examining the problem of stratification. The technique of stratification improves the
precision of estimate of character under study provided it is possible to choose an appropriate character highly
correlated with the character under study for the purpose of stratification.
Hidiroglou and Sarndal (1998) said that double sampling is a cost effective sampling design, and
precision of ratio and regression estimates of study variable under two-phase sampling increases if there is a
high degree of correlation between the auxiliary variable and study variable.
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Double sampling is usually presented under the assumption that one of the samples is nested within the
other; this method in particular is known as two-phase sampling, which is precisely a sampling scheme
involving two distinct phases, in the first of which information about (a) particular variable(s) of interest (the
auxiliary information) is collected on all members of the sample, and in the second, information about other
variables is collected on a sub-sample of the individuals in the original sample.
This technique consists of taking a large sample of size
by simple random sampling without
replacement (SRSWOR) to estimate population mean of auxiliary variable while a sub-sample of out of units
is drawn by SRSWOR to observe the characteristic under study.
The use of the auxiliary information in ratio estimation can be used to achieve greater precision in
estimation as it takes advantage of the correlation between the auxiliary variate
and the variate of
interest
. When information about the auxiliary variable is available, it increases the precision of the
population variance.
Snedecor and King (1942) mentioned the application of a two-phase sampling procedure for
determination of corn yield. They found out that it was easier and cheaper to count the number of ears of corn in
a given unit area than to harvest the yield and obtain the dry weight of kernels. The high cost of making dry
weight determination led to the use of two-phase sampling in which ears were not counted and measured in
many fields but harvested in only a portion of fields, thus, taking advantage of the correlation between study
variable (the dry weight of kernels) and the auxiliary variable(length-diameter of the ear).
Hartley and Ross (1954) developed exact ratio estimator. Freese (1962) presented detailed description
of its application in forestry. He selected an auxiliary variable to achieve primary objective of two-phase
sampling i.e. to reduce total inventory time without sacrificing the precision about the point estimate. Basal area
is commonly utilized as auxiliary variable with two-phase sampling for volume estimates. This is because of the
high correlation between basal area and volume and also the fact that basal area can be determined very quickly.
Singh et al. (2007) have suggested modified ratio estimators by using different pairs of known value of
population parameter(s).Singh et al. (2008) then proposed the Almost Unbiased (AU) estimator, a modification
which utilises a linear combination of the usual ratio estimator and the exponential estimator due to Bahl and
Tuteja (1991).
II.
MATERIALS AND METHODS
The data utilised in this study is primary in nature. It was obtained at the Oluwa Forest Reserve (OFR).
The study sites are located within Oluwa Forest Reserve (OFR). OFR is located in Odigbo Local government
area of Ondo state, Nigeria within latitudes 06 0 52' and 70 20’ N;and longitude 30 45’ and 40 32’ E. Oluwa Forest
Reserve is approximately 828 km2 with much of it lying approximately between 300 and 600m above sea level
(Iloeje 1981).
The sampling frame consists of a population of 481 trees, in which 204 initial sample trees of Teak
were measured for diameter-at-breast height (DBH) alone and subsequently, 60 second stage samples were then
measured for both the Total Height of Tree (THT) and DBH. Both samples were collected using SRSWOR and
their sizes determined from a pilot survey of 30 trees using an optimum allocation in two-phase sampling
method.
The correlation value which is used in analysing the improvement in efficiency due to ratio
estimation and also determining the optimal samples sizes was obtained from a pilot survey of 30 trees in which
information was collected on both Diameter-at-Breast Height (DBH) and the Total Height of Tree (THT).
2.1 The Almost Unbiased (AU) Ratio Estimator
Considering a finite population;
of
units. Let
denote the
values of the units included in a sample of size n, drawn by SRSWOR.
In order to have a survey estimate of the population mean of the study character y, assuming the knowledge of
the population mean of the auxiliary character x, we have;
(1)
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3. On The Use of an Almost Unbiased Ratio...
And
Such that
the population mean .
, where
denotes the set of all possible ratio type estimators for estimating
The Almost Unbiased (AU) estimator is given as;
And this is a linear variety if;
for
Where
denotes statistical constants and denotes the set of real numbers.
Noting that setting the constant and in
to 1 and 0 respectively, transforms
to the usual ratio
estimator,
and
correspondingly to the Bahl and Tuteja (1991) estimator
.
2.2 The AU ratio estimator in two-phase sampling
When is unknown, it is sometimes estimated from a preliminary large sample of size
only the characteristic
Let
denote the sample mean of
, be the sample means of
In two-phase sampling, the estimator
and
based on the first sample of size
,
and
respectively, based on a second phase sample of size
(7)
(8)
and
.
Noting that setting the constants
ratio estimator,
.
will take the following form;
for
Where
on which
and
and
in the above equations to 1 and 0, transforms
correspondingly to the Bahl and Tuteja (1991) estimator
to the usual
, both for use in
two-phase sampling.
The estimators with their biases and MSE’s (under two-phase sampling) are given below:
The Usual Ratio Estimator
(9)
(10)
(11)
Where;
,
,
,
The Bahl and Tuteja (1991) Estimator
(12)
(13)
(14)
The Almost Unbiased Estimator
(15)
(16)
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4. On The Use of an Almost Unbiased Ratio...
Given that:
,
and
Where:
(17)
(18)
(19)
(20)
2.3 Analysis of Improvement In Efficiency Due To Ratio Estimation:
From Cochran (1977), it is shown that the ratio estimator improves efficiency if;
(21)
Hence, data obtained from the study site pilot survey, shows that the ratio estimator which is being
considered for use in estimating the population total in this research will improve efficiency as compared to the
mean per unit estimator .
2.4 Critical range of assumed Costs:
Using an assumed cost of
, being the cost for obtaining information on DBH and a
cost
, for the THT of each tree during the pilot survey.
Equation (22) from Cochran (1977)gives the critical ranges of
sampling more profitable.
for a given
that makes two-phase
(22)
It is shown that the assumed costs satisfies the critical ranges
profitable for a given
To compute for the optimum sample sizes, we have;
that make double sampling more
(23)
(24)
(23) and (24) due to Raj and Chandhok (1998), are the optimal values of and given a correlation
value between the auxiliary variable and the study variable. Hence, and were conveniently chosen as 204
and 60 respectively.
Based on the first phase sample, the following parameters were estimated as shown in Table 1.
Table 1: Descriptive Statistics of The First Phase Samples
Mean
Name
Variable
Variance
Standard Deviation
Diam.-at-Breast Height (DBH)
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A second phase sample of size 60 was taken from the first phase sample , which records only the
measure of height (in metres) of the study variable , the following parameters were measured and are shown in
Table 2.
Name
DBH
THT
Table 2:Descriptive Statistics of The Second Phase Samples
Variable
Variance
Stand. Deviation
Mean
60
60
III. RESULTS AND DISCUSSION
The values of the Average Height of Teak trees in Oluwa Forest Reserve, obtained using the various
estimators considered in this study is summarized in the table below.
Table 3: Values of The Population Mean Obtained Using Different Estimators
Estimator Population Mean Estimates
18.039
20.957
20.40869
21.6010
The Bias, a measure of deviation of the estimates from the true value of the population parameter under
study in this research work are presented in Table 5.1.2 below;
Table 4: Bias of Estimators
Estimator
0.009612
0.001161
0.00000179
Table 5 shows the Variance of the unbiased mean per unit estimator
(MSEs) of the usual ratio estimator
, the Bahl and Tuteja (1991) estimator
and Mean Square Errors
and the AU ratio estimator.
Table 5: Variance/MSEs of Estimators
Estimators
0.372194
0.1997189
0.1586222
0.14407924
The inequality below shows the relationship among various estimators being considered;
This indicates an agreement with previous literatures which holds that the Bahl & Tuteja (1991)
estimator
is more precise than the usual ratio estimator
.
The Percent Relative Gain in Efficiencies (PRGEs) of the estimators
computed with respect to the unbiased estimator
all
are listed in Table 6.
Table 6: Percent Relative Gain in Efficiencies (PRGE) of Estimators
Estimators
46.34
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6. On The Use of an Almost Unbiased Ratio...
57.38
61.29
The correlation between the variables THT And DBH is strong and positive with a value of
The estimate of the population mean height of trees in the Oluwa forest reserve, Ondo State, Nigeria,
using the almost unbiased ratio estimator is 21.601m
The Mean Squared Error (MSE) of the estimates based on the AU estimator is relatively lowest with
respect to the Bahl and Tuteja (1991) estimator and the usual ratio estimator, all under the two-phase
sampling scheme and given the same assumed costs.
IV.
CONCLUSION
This shows clearly that the Almost Unbiased Ratio Estimator
considered with considerable gain in efficiency.
is more efficient than other estimators
Hence we can conclude that the estimator
is to be preferred in practice over the unbiased
estimator , the usual ratio estimator
and the Bahl and Tuteja (1991) estimator
, when estimating
population means, not only in agricultural studies but also in food processing, education and other sectors where
a decent level of precision is needed for obtaining estimates required for planning purposes and decision
making.
For further study, one would suggest the development of a modification of the AU estimator for use in
stratification, such that the enhancement of precision which come about as result of classifying the population
based on their shared attributes could be exploited.
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