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.
IRJET- Analysis of Crop Yield Prediction using Data Mining Technique to Predi...IRJET Journal
This document discusses using data mining techniques to predict annual crop yields in India. It begins with an abstract that outlines how agriculture is important to the Indian economy but crop production depends on seasonal and environmental factors, making yield prediction challenging. The document then provides an introduction to data mining and its potential application to predict crop yields. It reviews literature on using various data mining methods like linear regression and k-nearest neighbor algorithms to predict yields of major crops in India based on historical data on climate, soil conditions and more. The goal is to help farmers choose optimal crops and improve farm productivity and profits.
IRJET- Smart Farming Crop Yield Prediction using Machine LearningIRJET Journal
The document proposes a method for smart farming and crop yield prediction using machine learning algorithms like Support Vector Machine and Random Forest. Historical agricultural data on factors like moisture, rainfall, temperature and humidity is collected and analyzed to predict crop yields and whether conditions will be excellent, good, or poor. The goal is to help farmers increase profits by providing insights into how environmental conditions impact crops.
IRJET- Agricultural Crop Yield Prediction using Deep Learning ApproachIRJET Journal
This document discusses using artificial neural networks to predict agricultural crop yields. It begins with an abstract that outlines using ANNs to predict crop yield given various input parameters like soil pH, nitrogen levels, temperature, rainfall, etc. It then provides an introduction on the importance of accurate crop yield prediction. The next sections discuss literature on previous ANN crop yield prediction models, the proposed ANN approach including network architecture and activation functions, the design process, and conclusions. The key points are that ANNs can accurately predict crop yields given various climatic and soil inputs, and providing farmers with these predictions could help maximize profits and minimize losses.
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.
IRJET - Analysis of Crop Yield Prediction by using Machine Learning AlgorithmsIRJET Journal
This document analyzes crop yield prediction using machine learning algorithms like K-Nearest Neighbor and Support Vector Machine. It discusses collecting agricultural data from various regions on factors like rainfall, humidity, temperature, area, yield, soil type and location. The data is preprocessed, transformed and split into training and testing sets. Both KNN and SVM are applied to the data and SVM is found to have higher accuracy and faster execution time compared to KNN in predicting suitable crops and estimated yields. The proposed system provides farmers an efficient way to predict crops and yields for their region using modern machine learning techniques.
Crop Recommendation System to Maximize Crop Yield using Machine Learning Tech...IRJET Journal
This document describes a crop recommendation system that uses machine learning techniques to maximize crop yields. The system collects soil data from testing labs and combines the data with crop information from experts. It then uses an ensemble model with majority voting to recommend crops for specific soil parameters. The ensemble uses support vector machine and artificial neural network learners to make recommendations with high accuracy. The goal is to help farmers choose crops best suited to their soil needs and increase productivity.
IRJET - Enlightening Farmers on Crop YieldIRJET Journal
This document discusses using data mining techniques to predict crop yields to help farmers. It proposes using a random forest regression algorithm on past agricultural data from 2000-2014 to build a prediction model. The model would help farmers select optimal crops, understand weather patterns, and maximize yields. The system is described as gathering data, preprocessing it, training a random forest model on 60% of the data and testing it on 20%. It would then provide yield predictions and recommendations to farmers through a visualization tool. The goal is to help guide farmers' decisions around fertilizer use, soil management, and crop selection to improve production levels.
The document summarizes a seminar presentation on crop prediction using an artificial neural network approach. It discusses using parameters like soil pH, nitrogen, phosphate, potassium, sunshine hours, rainfall and temperature as inputs to an ANN model to predict suitable crops. The backpropagation algorithm is used to train the multi-layer neural network to minimize error. The design flow involves data collection, building a prediction model through classification, and suggesting fertilizers. Future work may include developing regional language applications, detecting crop diseases, considering more parameters, and providing micronutrient information. The ANN approach is concluded to be a beneficial tool for crop prediction.
IRJET- Analysis of Crop Yield Prediction using Data Mining Technique to Predi...IRJET Journal
This document discusses using data mining techniques to predict annual crop yields in India. It begins with an abstract that outlines how agriculture is important to the Indian economy but crop production depends on seasonal and environmental factors, making yield prediction challenging. The document then provides an introduction to data mining and its potential application to predict crop yields. It reviews literature on using various data mining methods like linear regression and k-nearest neighbor algorithms to predict yields of major crops in India based on historical data on climate, soil conditions and more. The goal is to help farmers choose optimal crops and improve farm productivity and profits.
IRJET- Smart Farming Crop Yield Prediction using Machine LearningIRJET Journal
The document proposes a method for smart farming and crop yield prediction using machine learning algorithms like Support Vector Machine and Random Forest. Historical agricultural data on factors like moisture, rainfall, temperature and humidity is collected and analyzed to predict crop yields and whether conditions will be excellent, good, or poor. The goal is to help farmers increase profits by providing insights into how environmental conditions impact crops.
IRJET- Agricultural Crop Yield Prediction using Deep Learning ApproachIRJET Journal
This document discusses using artificial neural networks to predict agricultural crop yields. It begins with an abstract that outlines using ANNs to predict crop yield given various input parameters like soil pH, nitrogen levels, temperature, rainfall, etc. It then provides an introduction on the importance of accurate crop yield prediction. The next sections discuss literature on previous ANN crop yield prediction models, the proposed ANN approach including network architecture and activation functions, the design process, and conclusions. The key points are that ANNs can accurately predict crop yields given various climatic and soil inputs, and providing farmers with these predictions could help maximize profits and minimize losses.
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.
IRJET - Analysis of Crop Yield Prediction by using Machine Learning AlgorithmsIRJET Journal
This document analyzes crop yield prediction using machine learning algorithms like K-Nearest Neighbor and Support Vector Machine. It discusses collecting agricultural data from various regions on factors like rainfall, humidity, temperature, area, yield, soil type and location. The data is preprocessed, transformed and split into training and testing sets. Both KNN and SVM are applied to the data and SVM is found to have higher accuracy and faster execution time compared to KNN in predicting suitable crops and estimated yields. The proposed system provides farmers an efficient way to predict crops and yields for their region using modern machine learning techniques.
Crop Recommendation System to Maximize Crop Yield using Machine Learning Tech...IRJET Journal
This document describes a crop recommendation system that uses machine learning techniques to maximize crop yields. The system collects soil data from testing labs and combines the data with crop information from experts. It then uses an ensemble model with majority voting to recommend crops for specific soil parameters. The ensemble uses support vector machine and artificial neural network learners to make recommendations with high accuracy. The goal is to help farmers choose crops best suited to their soil needs and increase productivity.
IRJET - Enlightening Farmers on Crop YieldIRJET Journal
This document discusses using data mining techniques to predict crop yields to help farmers. It proposes using a random forest regression algorithm on past agricultural data from 2000-2014 to build a prediction model. The model would help farmers select optimal crops, understand weather patterns, and maximize yields. The system is described as gathering data, preprocessing it, training a random forest model on 60% of the data and testing it on 20%. It would then provide yield predictions and recommendations to farmers through a visualization tool. The goal is to help guide farmers' decisions around fertilizer use, soil management, and crop selection to improve production levels.
The document summarizes a seminar presentation on crop prediction using an artificial neural network approach. It discusses using parameters like soil pH, nitrogen, phosphate, potassium, sunshine hours, rainfall and temperature as inputs to an ANN model to predict suitable crops. The backpropagation algorithm is used to train the multi-layer neural network to minimize error. The design flow involves data collection, building a prediction model through classification, and suggesting fertilizers. Future work may include developing regional language applications, detecting crop diseases, considering more parameters, and providing micronutrient information. The ANN approach is concluded to be a beneficial tool for crop prediction.
IRJET- Survey on Crop Suggestion using Weather AnalysisIRJET Journal
The document discusses a proposed model to predict the most suitable crop for a given location based on weather analysis and soil parameters. It would use fuzzy logic, Gradient Boosted Decision Tree (GBDT) algorithm, and R Neuralnet Package. The model aims to address the problems of crop failure, food shortage, and increasing farmer suicides by recommending crops suited to the climatic conditions and soil quality of a particular site. It would provide suggestions on both crop yield and suitable crop types to maximize agricultural productivity. The inputs to the system would be meteorological and soil data, and it would analyze past and future weather data to recommend crops.
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.
IRJET- Survey of Estimation of Crop Yield using Agriculture DataIRJET Journal
This document discusses using data mining techniques to analyze agricultural data and estimate crop yields. It begins with an introduction to big data and data preprocessing techniques. The main goals of data cleaning are discussed, including filtering noise from datasets. Common data mining tasks like classification, regression, and clustering are also overviewed. Previous studies on crop yield prediction using clustering algorithms and weather data are summarized. The proposed system would use the DBSCAN clustering algorithm to analyze various agricultural datasets to more optimally and accurately estimate crop yields. This could help farmers make better decisions to increase production.
Crop Selection Method Based on Various Environmental Factors Using Machine Le...IRJET Journal
This document proposes two crop selection methods using machine learning:
1. A Crop Selection Method that uses classification algorithms to select the most suitable crop based on environmental and economic factors like temperature, rainfall, soil type, and market prices.
2. A Crop Sequencing Method that uses a crop sequencing algorithm to suggest an optimal sequence of crops over a growing season based on predicted yield rates and market prices to maximize profits. Both methods use a machine learning tool called WEKA and historical crop data to make predictions.
IRJET - Agricultural Analysis using Data Mining TechniquesIRJET Journal
This document discusses using data mining techniques like K-means clustering, KNN classification, Bayesian networks, and support vector machines to analyze agricultural data and predict crop production. It analyzes data related to rainfall, temperature, area planted, and production for various crops. These techniques are applied to the agricultural data set to accurately predict future crop yields. Data mining plays an important role in solving problems in agriculture by analyzing large datasets and identifying patterns.
This document discusses using machine learning techniques to build models for predicting agricultural crop yields. It first provides background on the importance of feature selection and engineering for accurate modeling. It then outlines the proposed system, which would acquire dataset on weather, soil parameters, and past yields, preprocess the data, perform clustering, feature selection, and classification to predict future crop yields. The goal is to help farmers choose optimal crops and improve farm management. The document reviews several related works applying data mining and machine learning to agricultural data and concludes the proposed approach could effectively optimize feature selection and model performance.
This document discusses a proposed system called the Farmer's Analytical Assistant, which aims to help farmers in India maximize crop yields through predictive analysis and recommendations. It analyzes agricultural data on factors like soil properties, rainfall, and past crop performance using machine learning techniques to predict optimal crops for different regions based on the environmental conditions. The proposed system would allow farmers to input local data, receive personalized yield predictions and crop suggestions, and get advice from experts online. The methodology section describes how climate/rainfall and soil data is collected and analyzed using machine learning models to provide crop recommendations. The goal is to improve upon traditional crop selection methods and help increase farmers' incomes.
This paper is the continuation of the paper published by the authors Arun Balaji and Baskaran [2].
Multiple linear regression (MLR) equations were developed between the years of rice cultivation and Feed
Forward Back Propagation Neural Network (FFBPNN) method of predicted area of rice cultivation / rice
production for different districts pertaining to Kuruvai, Samba and Kodai seasons in Tamilnadu. The
average r2 value in area of cultivation is 0.40 in Kuruvai season, 0.42 in Samba season and 0.46 in Kodai
season, where as the r2 value in rice production is 0.31 in Kuruvai season, 0.23 in Samba season and
0.42 in Kodai season. The Rice Data Simulator (RDS) predicted the area of rice cultivation and rice
production using the MLR equations developed in this research. The range of average predicted area for
Kuruvai, Samba and Kodai seasons varies from 12052.52 ha to 13595.32 ha, 48998.96 ha to 53324.54 ha
and 4241.23 ha to 6449.88 ha respectively whereas the range of average predicted rice production varies
from 45132.88 tonnes to 46074.48 tonnes in Kuruvai, 128619 tonnes to 139693.29 tonnes in Samba and
15446.07 to 20573.50 tonnes in Kodai seasons. The mean absolute relative error (ARE) between the
FFBPNN and multiple regression methods of prediction of area of rice cultivation was found to be 15.58%,
8.04% and 26.34% for the Kuruvai, Samba and the Kodai seasons respectively. The ARE for the rice
production was found to be 17%, 11.80% and 24.60% for the Kuruvai, Samba and the Kodai seasons
respectively. The paired t test between the FFBPNN and MLR methods of predicted area of cultivation in
Kuruvai shows that there is no significant difference between the two types of prediction for certain
districts.
Forest Area Estimation in Kutai Nasional Park of East Kalimantan Using Comput...Editor IJCATR
The paper presents design of computer system application for the forest area estimation using the combination of genetic
algorithm (GA) and support vector machine SVM) methods in Kutai National park of East Borneo. The considering variables such as
reboization concerning natural green, forest fire, encroachment and illegal logging activities are the basic data for our proposed design. In
the development of design computer system application, the Unified Modeling Language is adopted with the use case, activity diagrams and
sequence diagrams are systematically followed in order to keep the purpose of design on track. The supporting instrument for this research is
the language programming of Borland Delphi 7 and MySQL database. The accuracy of area estimation result is compared with the actual
data using mean absolute percentage error (MAPE).
Predicting food demand in food courts by decision tree approachesSelman Bozkır
The document discusses using decision trees to predict food demand. It introduces data mining and decision tree methods like CART, CHAID, and Microsoft Decision Trees. The study used these methods to build models to predict food sales for different customer types using variables like day, month, menu items, and holidays. CHAID achieved the best average accuracy of 69.5% compared to MSDT and CART. While CHAID performed best, multi-way decision trees can also perform well, and more data could improve results. A decision tree powered web system could help with food demand prediction.
IRJET- Weather Prediction for Tourism Application using ARIMAIRJET Journal
This document discusses using an ARIMA model to predict weather patterns for tourism applications. It begins with an introduction to weather forecasting and its importance for the tourism industry. It then reviews related work on weather prediction using machine learning methods. The proposed method involves collecting weather data, preprocessing it, converting it to a stationary time series, analyzing it using an ARIMA model, and concluding that ARIMA can accurately predict weather patterns to help tourists plan trips based on the forecast.
Jianqiang Ren_Simulation of regional winter wheat yield by EPIC model.pptgrssieee
The document describes a study that used a crop growth model combined with remotely sensed leaf area index (LAI) data to simulate regional winter wheat yield in northern China. The study area covered 11 counties. Researchers used the EPIC crop growth model optimized with the SCE-UA algorithm. Remotely sensed MODIS LAI data were input to the model. The model was able to accurately simulate winter wheat sowing dates, plant density, fertilizer application rates, and yields compared to field investigation data, demonstrating the potential of the approach for crop monitoring and yield forecasting.
IRJET- Estimation of Nitrogen Content in Maize Leaves using Image Processing ...IRJET Journal
This document presents a method to estimate the nitrogen content in maize leaves using image processing techniques. Images of maize leaves are taken under different lighting conditions and preprocessed to remove noise. Color and texture features like entropy, mean, variance and average energy are extracted from the images. A regression model is developed to correlate these image features with nitrogen content values obtained from chemical tests. The regression model can then be used to estimate nitrogen content from new leaf images based on their extracted features. The proposed method provides a faster estimation of nitrogen compared to traditional chemical tests and may help optimize crop yields.
IRJET- Price Forecasting System for Crops at the Time of SowingIRJET Journal
1. The document proposes a price forecasting system for crops in India that uses past price data and a ARIMA (Auto Regressive Integrated Moving Average) model for time series analysis.
2. It analyzes factors like weather, soil conditions, production levels that affect crop prices. The proposed system would predict prices at the time of sowing based on these factors.
3. Preliminary results show the ARIMA model has potential to predict crop prices up to 95% accuracy and will improve with more daily price data. Accurate forecasts could help farmers and policymakers.
Optimum combination of farm enterprises among smallholder farmers in umuahia ...Alexander Decker
The document presents the results of a study that used linear programming to determine the optimal combination of farm enterprises for smallholder farmers in Umuahia Agricultural Zone, Abia State, Nigeria. A sample of 30 farmers was used to develop a model that maximized gross margin subject to resource constraints. The optimal plan included one crop enterprise, two crop mixtures, and two livestock enterprises. Sensitivity analysis found that increasing land by 25% increased gross margin by 13.48%, while increasing labor by 25% increased gross margin by 3.04%. The study recommends adopting more land and labor-saving technologies to improve farm production.
This document summarizes different analytical models that can be used for crop prediction, including classification models. It discusses feature selection methods like principal component analysis and information gain that are important for crop prediction models. The document reviews different machine learning techniques used in previous studies for crop yield prediction, such as linear regression, k-nearest neighbors, neural networks, support vector machines, and decision trees. It aims to compare the performance of these classification techniques for predicting crop yields based on parameters like temperature, rainfall, soil characteristics, and more.
A COMPREHENSIVE SURVEY ON AGRICULTURE ADVISORY SYSTEMIRJET Journal
This document provides a literature review and proposed methodology for an agricultural advisory system using data science techniques. It discusses several past studies that used machine learning algorithms like Naive Bayes, KNN, decision trees, and clustering for crop prediction and recommendations. The proposed system would collect agricultural data on parameters like rainfall, temperature and soil composition. It would preprocess, train and apply a supervised learning algorithm like Naive Bayes to provide priority-based crop recommendations to farmers based on location and year. The goal is to help farmers select suitable high-profit crops using data-driven techniques.
This document discusses how precision farming and big data can help improve agriculture. It notes that a majority of India's population depends on agriculture but farmers often lack information which can hurt crop yields. New technologies using sensors, cloud computing, and mobile phones can now provide farmers real-time data on soil conditions, weather, and crop health to help maximize production. Data mining techniques like classification and clustering can analyze large agricultural data sets to predict outcomes and identify patterns. This information can help farmers choose optimal crops and growing practices and help businesses anticipate supply and demand trends to better match production and pricing.
IMPLEMENTATION PAPER ON AGRICULTURE ADVISORY SYSTEMIRJET Journal
This document presents an implementation paper on an agriculture advisory system that uses machine learning algorithms to predict optimal crops and recommend fertilizers. It first reviews previous literature on similar crop prediction systems using data mining techniques. It then describes the proposed system's methodology, which involves 8 steps: data collection, preprocessing, training, supervised learning using Naive Bayes for crop prediction and KNN for fertilizer recommendation, priority-based crop recommendation, location- and year-based recommendations, output of results, and visual representation of recommendations. The system aims to help farmers select profitable crops and increase agricultural output by providing customized recommendations based on soil analysis and other input data. It concludes the proposed system could help address issues farmers face by streamlining information and facilitating efficient
An Overview of Crop Yield Prediction using Machine Learning ApproachIRJET Journal
This document discusses using machine learning approaches to predict crop yields. It provides an overview of previous research that has used techniques like random forest regressors, decision trees, and neural networks to predict yields based on environmental and historical data. The document also summarizes several studies that evaluated different machine learning algorithms for crop yield prediction and found random forest to often provide the most accurate forecasts. Improving yield prediction can help farmers select optimal crops and farming practices.
Agriculture crop yield prediction using inertia based cat swarm optimizationIJECEIAES
Crop yield prediction is among the most important and main sources of income in the Indian economy. In this paper, the improved cat swarm optimization (ICSO) based recurrent neural network (RNN) model is proposed for crop yield prediction using time series data. The inertia weight parameter is added to position equation that is selected randomly, and a new velocity equation is produced which enhances the searching ability in the best cat area. By using inertia weight, the ICSO enhances performance of feature selection and obtains better convergence in minimum iteration. The RNN is applied to produce direct graph using sequence of data and decides current layer output by involving all other existing calculations. The performance of the model is estimated using coefficient of determination (R2), root mean square error (RMSE), mean squared error (MSE), and mean absolute error (MAE) on the yield from the years 2011 to 2021 with an annual prediction for 120 records of approximately 8 million nuts. The evaluated result shows that the proposed ICSO-RNN model delivers metrics such as R2, MAE, MSE, and RMSE values of 0.99, 0.77, 0.68, and 0.82 correspondingly, which ensures accurate yield prediction when compared with the existing methods which are hybrid reinforcement learning-random forest (RL-RF) and machine learning (ML) methods.
Supervise Machine Learning Approach for Crop Yield Prediction in Agriculture ...IRJET Journal
This document summarizes a research paper that proposes using machine learning techniques like linear regression to predict crop yields in the agricultural sector. Specifically, it aims to build models using past agricultural data to help farmers predict yields before planting and determine how to allocate resources for maximum profit. The proposed system would analyze factors like environment, soil, water, temperature and use ML algorithms to forecast yields and provide smart recommendations to farmers. It discusses previous related work on crop yield prediction using machine learning and the limitations of existing systems. The goal is to develop an accurate predictive model using a supervised learning approach that farmers can use to make informed financial decisions.
IRJET- Survey on Crop Suggestion using Weather AnalysisIRJET Journal
The document discusses a proposed model to predict the most suitable crop for a given location based on weather analysis and soil parameters. It would use fuzzy logic, Gradient Boosted Decision Tree (GBDT) algorithm, and R Neuralnet Package. The model aims to address the problems of crop failure, food shortage, and increasing farmer suicides by recommending crops suited to the climatic conditions and soil quality of a particular site. It would provide suggestions on both crop yield and suitable crop types to maximize agricultural productivity. The inputs to the system would be meteorological and soil data, and it would analyze past and future weather data to recommend crops.
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.
IRJET- Survey of Estimation of Crop Yield using Agriculture DataIRJET Journal
This document discusses using data mining techniques to analyze agricultural data and estimate crop yields. It begins with an introduction to big data and data preprocessing techniques. The main goals of data cleaning are discussed, including filtering noise from datasets. Common data mining tasks like classification, regression, and clustering are also overviewed. Previous studies on crop yield prediction using clustering algorithms and weather data are summarized. The proposed system would use the DBSCAN clustering algorithm to analyze various agricultural datasets to more optimally and accurately estimate crop yields. This could help farmers make better decisions to increase production.
Crop Selection Method Based on Various Environmental Factors Using Machine Le...IRJET Journal
This document proposes two crop selection methods using machine learning:
1. A Crop Selection Method that uses classification algorithms to select the most suitable crop based on environmental and economic factors like temperature, rainfall, soil type, and market prices.
2. A Crop Sequencing Method that uses a crop sequencing algorithm to suggest an optimal sequence of crops over a growing season based on predicted yield rates and market prices to maximize profits. Both methods use a machine learning tool called WEKA and historical crop data to make predictions.
IRJET - Agricultural Analysis using Data Mining TechniquesIRJET Journal
This document discusses using data mining techniques like K-means clustering, KNN classification, Bayesian networks, and support vector machines to analyze agricultural data and predict crop production. It analyzes data related to rainfall, temperature, area planted, and production for various crops. These techniques are applied to the agricultural data set to accurately predict future crop yields. Data mining plays an important role in solving problems in agriculture by analyzing large datasets and identifying patterns.
This document discusses using machine learning techniques to build models for predicting agricultural crop yields. It first provides background on the importance of feature selection and engineering for accurate modeling. It then outlines the proposed system, which would acquire dataset on weather, soil parameters, and past yields, preprocess the data, perform clustering, feature selection, and classification to predict future crop yields. The goal is to help farmers choose optimal crops and improve farm management. The document reviews several related works applying data mining and machine learning to agricultural data and concludes the proposed approach could effectively optimize feature selection and model performance.
This document discusses a proposed system called the Farmer's Analytical Assistant, which aims to help farmers in India maximize crop yields through predictive analysis and recommendations. It analyzes agricultural data on factors like soil properties, rainfall, and past crop performance using machine learning techniques to predict optimal crops for different regions based on the environmental conditions. The proposed system would allow farmers to input local data, receive personalized yield predictions and crop suggestions, and get advice from experts online. The methodology section describes how climate/rainfall and soil data is collected and analyzed using machine learning models to provide crop recommendations. The goal is to improve upon traditional crop selection methods and help increase farmers' incomes.
This paper is the continuation of the paper published by the authors Arun Balaji and Baskaran [2].
Multiple linear regression (MLR) equations were developed between the years of rice cultivation and Feed
Forward Back Propagation Neural Network (FFBPNN) method of predicted area of rice cultivation / rice
production for different districts pertaining to Kuruvai, Samba and Kodai seasons in Tamilnadu. The
average r2 value in area of cultivation is 0.40 in Kuruvai season, 0.42 in Samba season and 0.46 in Kodai
season, where as the r2 value in rice production is 0.31 in Kuruvai season, 0.23 in Samba season and
0.42 in Kodai season. The Rice Data Simulator (RDS) predicted the area of rice cultivation and rice
production using the MLR equations developed in this research. The range of average predicted area for
Kuruvai, Samba and Kodai seasons varies from 12052.52 ha to 13595.32 ha, 48998.96 ha to 53324.54 ha
and 4241.23 ha to 6449.88 ha respectively whereas the range of average predicted rice production varies
from 45132.88 tonnes to 46074.48 tonnes in Kuruvai, 128619 tonnes to 139693.29 tonnes in Samba and
15446.07 to 20573.50 tonnes in Kodai seasons. The mean absolute relative error (ARE) between the
FFBPNN and multiple regression methods of prediction of area of rice cultivation was found to be 15.58%,
8.04% and 26.34% for the Kuruvai, Samba and the Kodai seasons respectively. The ARE for the rice
production was found to be 17%, 11.80% and 24.60% for the Kuruvai, Samba and the Kodai seasons
respectively. The paired t test between the FFBPNN and MLR methods of predicted area of cultivation in
Kuruvai shows that there is no significant difference between the two types of prediction for certain
districts.
Forest Area Estimation in Kutai Nasional Park of East Kalimantan Using Comput...Editor IJCATR
The paper presents design of computer system application for the forest area estimation using the combination of genetic
algorithm (GA) and support vector machine SVM) methods in Kutai National park of East Borneo. The considering variables such as
reboization concerning natural green, forest fire, encroachment and illegal logging activities are the basic data for our proposed design. In
the development of design computer system application, the Unified Modeling Language is adopted with the use case, activity diagrams and
sequence diagrams are systematically followed in order to keep the purpose of design on track. The supporting instrument for this research is
the language programming of Borland Delphi 7 and MySQL database. The accuracy of area estimation result is compared with the actual
data using mean absolute percentage error (MAPE).
Predicting food demand in food courts by decision tree approachesSelman Bozkır
The document discusses using decision trees to predict food demand. It introduces data mining and decision tree methods like CART, CHAID, and Microsoft Decision Trees. The study used these methods to build models to predict food sales for different customer types using variables like day, month, menu items, and holidays. CHAID achieved the best average accuracy of 69.5% compared to MSDT and CART. While CHAID performed best, multi-way decision trees can also perform well, and more data could improve results. A decision tree powered web system could help with food demand prediction.
IRJET- Weather Prediction for Tourism Application using ARIMAIRJET Journal
This document discusses using an ARIMA model to predict weather patterns for tourism applications. It begins with an introduction to weather forecasting and its importance for the tourism industry. It then reviews related work on weather prediction using machine learning methods. The proposed method involves collecting weather data, preprocessing it, converting it to a stationary time series, analyzing it using an ARIMA model, and concluding that ARIMA can accurately predict weather patterns to help tourists plan trips based on the forecast.
Jianqiang Ren_Simulation of regional winter wheat yield by EPIC model.pptgrssieee
The document describes a study that used a crop growth model combined with remotely sensed leaf area index (LAI) data to simulate regional winter wheat yield in northern China. The study area covered 11 counties. Researchers used the EPIC crop growth model optimized with the SCE-UA algorithm. Remotely sensed MODIS LAI data were input to the model. The model was able to accurately simulate winter wheat sowing dates, plant density, fertilizer application rates, and yields compared to field investigation data, demonstrating the potential of the approach for crop monitoring and yield forecasting.
IRJET- Estimation of Nitrogen Content in Maize Leaves using Image Processing ...IRJET Journal
This document presents a method to estimate the nitrogen content in maize leaves using image processing techniques. Images of maize leaves are taken under different lighting conditions and preprocessed to remove noise. Color and texture features like entropy, mean, variance and average energy are extracted from the images. A regression model is developed to correlate these image features with nitrogen content values obtained from chemical tests. The regression model can then be used to estimate nitrogen content from new leaf images based on their extracted features. The proposed method provides a faster estimation of nitrogen compared to traditional chemical tests and may help optimize crop yields.
IRJET- Price Forecasting System for Crops at the Time of SowingIRJET Journal
1. The document proposes a price forecasting system for crops in India that uses past price data and a ARIMA (Auto Regressive Integrated Moving Average) model for time series analysis.
2. It analyzes factors like weather, soil conditions, production levels that affect crop prices. The proposed system would predict prices at the time of sowing based on these factors.
3. Preliminary results show the ARIMA model has potential to predict crop prices up to 95% accuracy and will improve with more daily price data. Accurate forecasts could help farmers and policymakers.
Optimum combination of farm enterprises among smallholder farmers in umuahia ...Alexander Decker
The document presents the results of a study that used linear programming to determine the optimal combination of farm enterprises for smallholder farmers in Umuahia Agricultural Zone, Abia State, Nigeria. A sample of 30 farmers was used to develop a model that maximized gross margin subject to resource constraints. The optimal plan included one crop enterprise, two crop mixtures, and two livestock enterprises. Sensitivity analysis found that increasing land by 25% increased gross margin by 13.48%, while increasing labor by 25% increased gross margin by 3.04%. The study recommends adopting more land and labor-saving technologies to improve farm production.
This document summarizes different analytical models that can be used for crop prediction, including classification models. It discusses feature selection methods like principal component analysis and information gain that are important for crop prediction models. The document reviews different machine learning techniques used in previous studies for crop yield prediction, such as linear regression, k-nearest neighbors, neural networks, support vector machines, and decision trees. It aims to compare the performance of these classification techniques for predicting crop yields based on parameters like temperature, rainfall, soil characteristics, and more.
A COMPREHENSIVE SURVEY ON AGRICULTURE ADVISORY SYSTEMIRJET Journal
This document provides a literature review and proposed methodology for an agricultural advisory system using data science techniques. It discusses several past studies that used machine learning algorithms like Naive Bayes, KNN, decision trees, and clustering for crop prediction and recommendations. The proposed system would collect agricultural data on parameters like rainfall, temperature and soil composition. It would preprocess, train and apply a supervised learning algorithm like Naive Bayes to provide priority-based crop recommendations to farmers based on location and year. The goal is to help farmers select suitable high-profit crops using data-driven techniques.
This document discusses how precision farming and big data can help improve agriculture. It notes that a majority of India's population depends on agriculture but farmers often lack information which can hurt crop yields. New technologies using sensors, cloud computing, and mobile phones can now provide farmers real-time data on soil conditions, weather, and crop health to help maximize production. Data mining techniques like classification and clustering can analyze large agricultural data sets to predict outcomes and identify patterns. This information can help farmers choose optimal crops and growing practices and help businesses anticipate supply and demand trends to better match production and pricing.
IMPLEMENTATION PAPER ON AGRICULTURE ADVISORY SYSTEMIRJET Journal
This document presents an implementation paper on an agriculture advisory system that uses machine learning algorithms to predict optimal crops and recommend fertilizers. It first reviews previous literature on similar crop prediction systems using data mining techniques. It then describes the proposed system's methodology, which involves 8 steps: data collection, preprocessing, training, supervised learning using Naive Bayes for crop prediction and KNN for fertilizer recommendation, priority-based crop recommendation, location- and year-based recommendations, output of results, and visual representation of recommendations. The system aims to help farmers select profitable crops and increase agricultural output by providing customized recommendations based on soil analysis and other input data. It concludes the proposed system could help address issues farmers face by streamlining information and facilitating efficient
An Overview of Crop Yield Prediction using Machine Learning ApproachIRJET Journal
This document discusses using machine learning approaches to predict crop yields. It provides an overview of previous research that has used techniques like random forest regressors, decision trees, and neural networks to predict yields based on environmental and historical data. The document also summarizes several studies that evaluated different machine learning algorithms for crop yield prediction and found random forest to often provide the most accurate forecasts. Improving yield prediction can help farmers select optimal crops and farming practices.
Agriculture crop yield prediction using inertia based cat swarm optimizationIJECEIAES
Crop yield prediction is among the most important and main sources of income in the Indian economy. In this paper, the improved cat swarm optimization (ICSO) based recurrent neural network (RNN) model is proposed for crop yield prediction using time series data. The inertia weight parameter is added to position equation that is selected randomly, and a new velocity equation is produced which enhances the searching ability in the best cat area. By using inertia weight, the ICSO enhances performance of feature selection and obtains better convergence in minimum iteration. The RNN is applied to produce direct graph using sequence of data and decides current layer output by involving all other existing calculations. The performance of the model is estimated using coefficient of determination (R2), root mean square error (RMSE), mean squared error (MSE), and mean absolute error (MAE) on the yield from the years 2011 to 2021 with an annual prediction for 120 records of approximately 8 million nuts. The evaluated result shows that the proposed ICSO-RNN model delivers metrics such as R2, MAE, MSE, and RMSE values of 0.99, 0.77, 0.68, and 0.82 correspondingly, which ensures accurate yield prediction when compared with the existing methods which are hybrid reinforcement learning-random forest (RL-RF) and machine learning (ML) methods.
Supervise Machine Learning Approach for Crop Yield Prediction in Agriculture ...IRJET Journal
This document summarizes a research paper that proposes using machine learning techniques like linear regression to predict crop yields in the agricultural sector. Specifically, it aims to build models using past agricultural data to help farmers predict yields before planting and determine how to allocate resources for maximum profit. The proposed system would analyze factors like environment, soil, water, temperature and use ML algorithms to forecast yields and provide smart recommendations to farmers. It discusses previous related work on crop yield prediction using machine learning and the limitations of existing systems. The goal is to develop an accurate predictive model using a supervised learning approach that farmers can use to make informed financial decisions.
Crop Yield Prediction using Machine LearningIRJET Journal
This document discusses using machine learning techniques to predict crop yields. It begins with an abstract that outlines the importance of agriculture and maintaining crop production in India. The objectives are then stated as empowering farmers with knowledge of different crops and climate changes and overcoming obstacles by applying machine learning to predict crop yield based on factors like temperature, rainfall, and area. Related work on using climate data and machine learning algorithms like SVM and regression to predict yields is reviewed. The proposed system aims to select optimal crops for a land plot using techniques like XGBoost, Naive Bayes and SVM based on environmental variables. It is concluded that opportunities remain to enhance outcomes by considering all variables simultaneously and using larger datasets.
IRJET- Survey of Crop Recommendation SystemsIRJET Journal
This document summarizes and compares several papers on crop recommendation systems. It discusses papers that use techniques like artificial neural networks, ensemble models combining multiple algorithms like random trees and KNN, and algorithms like SVM. The document also compares the modules used in different systems like location detection, data analysis, similarity detection and recommendation generation. It concludes that using ensemble methods can improve accuracy over single algorithms and future work could integrate more factors like economic conditions and land area into recommendation systems.
Selection of crop varieties and yield prediction based on phenotype applying ...IJECEIAES
In India, agriculture plays an important role in the nation’s gross domestic product (GDP) and is also a part of civilization. Countries’ economies are also influenced by the amount of crop production. All business trading involves farming as a major factor. In order to increase crop production, different technological advancements are developed to acquire the information required for crop production. The proposed work is mainly focused on suitable crop selection across districts in Tamil Nadu, considering phenotype factors such as soil type, climatic factors, cropping season, and crop region. The key objective is to predict the suitable crop for the farmers based on their locations, soil types, and environmental factors. This results in less financial loss and a shorter crop production timeframe. Combined feature selection (CFS)-based machine regression helps increase crop production rates. A brief comparative analysis was also made between various machine learning (ML) regression algorithms, which majorly contributed to the process of crop selection considering phenotype factors. Stacked long short-term memory (LSTM) classifiers outperformed other decision tree (DT), k-nearest neighbor (KNN), and logistic regression (LR) with a prediction accuracy of 93% with the lowest classification accuracy metrics. The proposed method can help us select the perfect crop for maximum yield.
Famer assistant and crop recommendation systemIRJET Journal
This document describes a farmer assistant and crop recommendation system developed by researchers in India. The system uses machine learning algorithms to analyze soil quality parameters and weather data to recommend profitable crops for farmers to plant based on their location. It also predicts the best time and place for farmers to sell their crops to maximize profits. The system is designed as a web application that farmers can access using smartphones, providing crop recommendations, pricing information, and other services to help farmers increase yields and earnings. The researchers used an XGBoost classifier trained on agricultural data to predict soil fertility and suitable crops. The system aims to help farmers get higher profits through improved agricultural decision making.
This document summarizes a research paper that proposes a system to analyze crop phenology (growth stages) using IoT to support parallel agriculture management. The system would use sensors to collect data on soil moisture, temperature, humidity and other parameters. This data would be input to a database. Then, a multiple linear regression model trained on past data would predict the optimal crop and expected yield based on the tested sensor data and parameters. This system aims to help farmers select crops and fertilization practices tailored to their specific fields' conditions.
Statistical features learning to predict the crop yield in regional areasIJECEIAES
The plethora of information presented in the form of benchmark dataset plays a significant role in analyzing and understanding the crop yield in certain regions of regional territory. The information may be presented in the form of attributes makes a prediction of crop yield in various regions of machine learning. The information considered for processing involves data cleaning initially followed by binning to reduce the missing data. The information collected is subjected to clustering of data items based on patterns of similarity, The data items that are similar in nature is fed to the system with similarity measure, which involves understanding the distance of data items from its related data item leading to hyper parameters for analyzing of information while calculating the crop yield. The information may be used to ascertain the patterns of data that exhibit similarity with nearest neighbor represented by another attribute. Thus, the research method has yielded an accuracy of 89.62% of classification for predicting the crop yield in agricultural areas of Karnataka region.
GrowFarm – Crop, Fertilizer and Disease Prediction usingMachine LearningIRJET Journal
The document describes a proposed system called GrowFarm that uses machine learning to predict crops, fertilizers, and plant diseases. It aims to help farmers select optimal crops and fertilizers for their soil conditions, as well as identify diseases in plants. The system would take soil data and photos of plant leaves as input and provide recommendations. It reviews literature on existing crop recommendation systems that use techniques like decision trees, random forests, neural networks and ensemble methods. The proposed system architecture involves collecting soil parameters, recommending crops/fertilizers using machine learning models, and identifying diseases from plant photos using a deep learning model. It outlines the methodology as loading and pre-processing the dataset, then using it to train models for crop/fer
Crop Recommendation System Using Machine LearningIRJET Journal
The document describes a machine learning-based crop recommendation system that analyzes soil and climate data to predict the most suitable crops for farmers to grow. It evaluates several machine learning algorithms (decision tree, support vector machine, logistic regression, random forest) and finds that random forest has the highest accuracy at 99.09%. The system is implemented as a website using the random forest model to help farmers select optimal crops.
““Smart Crop Prediction System and Farm Monitoring System for Smart Farming””IRJET Journal
This document presents a smart crop prediction and farm monitoring system that uses machine learning and IoT technologies. The system aims to help farmers select suitable crops based on soil type and climate conditions. It analyzes data on soil properties, temperature, moisture and humidity to predict crop growth. It also develops a module for remote farm monitoring using sensors and a camera. The system is intended to guide farmers, especially small-scale farmers, in cultivating crops according to soil and weather conditions. It also notifies farmers if animals enter the farm or if the soil moisture level requires irrigation. The system uses techniques like CNN for crop prediction based on soil images and sends SMS alerts to farmers.
Application Of Machine Learning in Modern Agriculture for Crop Yield Predicti...IRJET Journal
This document proposes a machine learning model for crop yield prediction and fertilizer recommendations in agriculture. It discusses existing systems that focus on single crops or aspects of agriculture. The proposed system predicts crop type, fertilizer type, and fertilizer amount using multiple machine learning algorithms. It finds that stacking XGB and random forest models performs best for crop and fertilizer type prediction. Regression models best predict fertilizer amount, with XGB regression performing best. The system is intended to help farmers plan crops and increase yields. It is evaluated using real-world agricultural data and metrics, finding it can effectively predict crops, fertilizer needs, and amounts to assist modern agriculture.
Data driven algorithm selection to predict agriculture commodities priceIJECEIAES
Price prediction and forecasting are common in the agriculture sector. The previous research shows that the advancement in prediction and forecasting algorithms will help farmers to get a better return for their produce. The selection of the best fitting algorithm for the given data set and the commodity is crucial. The historical experimental results show that the performance of the algorithms varies with the input data. Our main objective was to develop a model in which the best-performing prediction algorithm gets selected for the given data set. For the experiment, we have used seasonal autoregressive integrated moving average (SARIMA) stack ensemble and gradient boosting algorithms for the commodities Tomato and Potato with monthly and weekly average prices. The experimental results show that no algorithm is consistent with the given commodities and price data. Using the proposed model for the monthly forecasting and Tomato, stack ensemble is a better choice for Karnataka and Madhya Pradesh states with 59% and 61% accuracy. For Potatoes with the monthly price for Karnataka and Maharashtra, the stack ensemble model gave 60% and 85% accuracy. For weekly prediction, the accuracy of gradient boosting is better compared to other models.
Internet of things (IoT) smart technology enables new digital agriculture. Technology has become necessary to address today's challenges, and many
sectors are automating their processes with the newest technologies. By maximizing fertiliser use to boost plant efficiency, smart agriculture, which is based on IoT technology, intends to assist producers and farmers in
reducing waste while improving output. With IoT-based smart farming, farmers may better manage their animals, develop crops, save costs, and
conserve resources. Climate monitoring, drought detection, agriculture and production, pollution distribution, and many more applications rely on the weather forecast. The accuracy of the forecast is determined by prior
weather conditions across broad areas and over long periods. Machine learning algorithms can help us to build a model with proper accuracy. As a result, increasing the output on the limited acreage is important. IoT smart farming is a high-tech method that allows people to cultivate crops cleanly
and sustainably. In agriculture, it is the use of current information and
communication technologies.
IRJET- Crop Prediction and Disease DetectionIRJET Journal
This document discusses a proposed system for crop prediction and disease detection using data mining techniques and image processing. The system would use algorithms like Apriori and C4.5 to predict crop yields based on past climate data like temperature and rainfall. It would also allow farmers to upload images of crop diseases to identify the disease and recommended treatments. The goal is to help farmers make better decisions around crop selection and disease management given expected climate conditions.
Similar to IRJET- Agricultural Data Modeling and Yield Forecasting using Data Mining Techniques (20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.