An Efficient and Novel Crop Yield Prediction Method using Machine Learning Al...IIJSRJournal
The process of examining, filtering, and presenting data to obtain valuable information and make decisions is known as information analysis. Food resources are in high demand in countries like India, where they serve the population and help to secure the nation's security. Crop production is largely influenced by weather variations, soil quality, water availability, and fertilizer application, among other factors. The various types of soil play a significant effect in agricultural production. Recommending fertilizers to agriculturists may assist them in making better crop selection and maintenance decisions. Crop yield prediction can be done using a variety of studies using information and communication technology (ICT). Different sorts of mining techniques for data analysis and data acquisition can be widely used for a variety of purposes. Smart agriculture is a method of transmitting data from average farmers to skilled farmers.
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.
Soil health analysis for crop suggestions using machine learningEditorIJAERD
Indian economy is depending on agriculture. Agriculture is the main source of income for most of the
population. So farmers are always curious about yield prediction. Many factors are responsible like soil, weather, rain,
fertilizers and pesticides to increase yield production. Agriculture being a soil-based industry, an increase in yield can
only be attained by ensuring that the soil provides a balanced and an adequate supply of nutrients. Soil testing is pivotal
in understanding the deficiencies in soil and avoiding nutrient imbalance. This survey and study focuses on the different
soil types, crop types and soil test reports. Soils are complex mixtures of air, water, minerals, organic matter, and
countless organisms that are the decaying remains of once-living things. We can say soil is an important ingredient of
agriculture. There are several types of soils and each type of soil can have different kinds of features and different kinds
of crops grow on different types of soils. We must know which type of crop is go better in our soil. We can apply machine
learning techniques to classify soil and to predict the crop suitable.
An Efficient and Novel Crop Yield Prediction Method using Machine Learning Al...IIJSRJournal
The process of examining, filtering, and presenting data to obtain valuable information and make decisions is known as information analysis. Food resources are in high demand in countries like India, where they serve the population and help to secure the nation's security. Crop production is largely influenced by weather variations, soil quality, water availability, and fertilizer application, among other factors. The various types of soil play a significant effect in agricultural production. Recommending fertilizers to agriculturists may assist them in making better crop selection and maintenance decisions. Crop yield prediction can be done using a variety of studies using information and communication technology (ICT). Different sorts of mining techniques for data analysis and data acquisition can be widely used for a variety of purposes. Smart agriculture is a method of transmitting data from average farmers to skilled farmers.
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.
Soil health analysis for crop suggestions using machine learningEditorIJAERD
Indian economy is depending on agriculture. Agriculture is the main source of income for most of the
population. So farmers are always curious about yield prediction. Many factors are responsible like soil, weather, rain,
fertilizers and pesticides to increase yield production. Agriculture being a soil-based industry, an increase in yield can
only be attained by ensuring that the soil provides a balanced and an adequate supply of nutrients. Soil testing is pivotal
in understanding the deficiencies in soil and avoiding nutrient imbalance. This survey and study focuses on the different
soil types, crop types and soil test reports. Soils are complex mixtures of air, water, minerals, organic matter, and
countless organisms that are the decaying remains of once-living things. We can say soil is an important ingredient of
agriculture. There are several types of soils and each type of soil can have different kinds of features and different kinds
of crops grow on different types of soils. We must know which type of crop is go better in our soil. We can apply machine
learning techniques to classify soil and to predict the crop suitable.
Crop Yield Prediction and Efficient use of Fertilizers
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.org
Crop modeling for stress situations, cropping system , assessing stress through remote sensing, understanding the adaptive features of crops for survival under stress .
To remain profitable in agriculture under the present condition every farmer should consider that fertility level must be measured, this presentation based on how recommendation gives based on soil testings.
A Review on Associative Classification Data Mining Approach in Agricultural S...Editor IJMTER
Data mining in agriculture is a very recent research topic. It consists in the
application of data mining techniques to agriculture. Recent technologies are nowadays able to
provide a lot of information on agricultural-related activities, which can then be analyzed in
order to find important information. A related, but not equivalent term is precision agriculture.
This research aimed to assess the various classification techniques of data mining and apply
them to a soil science database to establish if meaningful relationships can be found. A large
data set of soil database is extracted from the Soil Science & Agricultural department, Bhopal
M.P and National Informatics Centre, The application of data mining techniques has never
been conducted for Bhopal soil data sets. The research compares the different classifiers and
the outcome of this research could improve the management and systems of soil uses
throughout a large number of fields that include agriculture, horticulture, environmental and
land use management.
Simulation models of agricultural systems, when coupled with appropriate
data sources, have a great potential for bringing agricultural research and development into the age of information technology.
Crop modeling has been applied at various scales in agriculture, from precision farming, to farm planning, to watershed or regional policy development. Crop models are mechanistic process-based models in response to daily weather inputs, predict soil traits, daily photosynthesis, growth, and crop management.
Intelligent Chemical Fertilizer Recommendation System for Rice Fields IIJSRJournal
In this paper, a recommendation system for supplementary chemical fertilizers of rice fields is proposed using data mining methods. Traditionally, an expert determines the necessary amount of chemical fertilizer for each field after testing the amount of existing organic materials in the soil. The recommendation provided by the expert is a combination of agricultural science and region-specific conditions. In this paper, thru recognizing the existing pattern in recommendations proposed by two groups of experts for the agricultural lands in Mazandaran Province in recent years, a predictive model is proposed. Different artificial intelligence techniques are compared with each other and the best one among them is introduced
Implemented various classification models using R language to identify which one performs best for prediction of soil fertility and which properties are important in defining the fertility of soil.
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.
Crop Yield Prediction and Efficient use of Fertilizers
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.org
Crop modeling for stress situations, cropping system , assessing stress through remote sensing, understanding the adaptive features of crops for survival under stress .
To remain profitable in agriculture under the present condition every farmer should consider that fertility level must be measured, this presentation based on how recommendation gives based on soil testings.
A Review on Associative Classification Data Mining Approach in Agricultural S...Editor IJMTER
Data mining in agriculture is a very recent research topic. It consists in the
application of data mining techniques to agriculture. Recent technologies are nowadays able to
provide a lot of information on agricultural-related activities, which can then be analyzed in
order to find important information. A related, but not equivalent term is precision agriculture.
This research aimed to assess the various classification techniques of data mining and apply
them to a soil science database to establish if meaningful relationships can be found. A large
data set of soil database is extracted from the Soil Science & Agricultural department, Bhopal
M.P and National Informatics Centre, The application of data mining techniques has never
been conducted for Bhopal soil data sets. The research compares the different classifiers and
the outcome of this research could improve the management and systems of soil uses
throughout a large number of fields that include agriculture, horticulture, environmental and
land use management.
Simulation models of agricultural systems, when coupled with appropriate
data sources, have a great potential for bringing agricultural research and development into the age of information technology.
Crop modeling has been applied at various scales in agriculture, from precision farming, to farm planning, to watershed or regional policy development. Crop models are mechanistic process-based models in response to daily weather inputs, predict soil traits, daily photosynthesis, growth, and crop management.
Intelligent Chemical Fertilizer Recommendation System for Rice Fields IIJSRJournal
In this paper, a recommendation system for supplementary chemical fertilizers of rice fields is proposed using data mining methods. Traditionally, an expert determines the necessary amount of chemical fertilizer for each field after testing the amount of existing organic materials in the soil. The recommendation provided by the expert is a combination of agricultural science and region-specific conditions. In this paper, thru recognizing the existing pattern in recommendations proposed by two groups of experts for the agricultural lands in Mazandaran Province in recent years, a predictive model is proposed. Different artificial intelligence techniques are compared with each other and the best one among them is introduced
Implemented various classification models using R language to identify which one performs best for prediction of soil fertility and which properties are important in defining the fertility of soil.
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.
Pesticide recommendation system for cotton crop diseases due to the climatic ...IJMREMJournal
Data mining is a process of extracting knowledge from a vast database using tools and techniques. Data
mining plays an important role in decision making on issues related to many real-time problems such as
business, education, agriculture etc. Data mining in agriculture helps the farmers to decide on crop yield ratio,
water resource management, pesticides management and fertilizer management. Nowadays, climatic change is
one of the challenging problems in agriculture which has a greater impact on productivity. Many
researchers have contributed in the field of agriculture data mining i) To predict crop productivity, ii) water
management, iii) air pollution using the naïve bias and decision tree algorithms. The Proposed work is to
predict the diseases due to Climatic changes and recommended pesticide for the disease. Decision tree
algorithm is used to develop a recommendation system which helps to the farmer in the usage of pesticide for
the incidence of crop diseases.
This is about survey the crop yield prediction using some data mining classification methods namely prdiction with classification,residue climate control, feature selection extraction, crop classification models,evaluation metrics, accuracy level,classification decision, result analysis,rain fall pH, principal component analysis, information gain
CROP MODELING IN VEGETABLES ( AABID AYOUB SKUAST-K).pptxAabidAyoub
crop modeling is future in agriculture to tackle changing environment conditions and increase food security in the world. These models incorporate various factors such as climate, soil characteristics, agronomic practices, and crop physiology to predict crop yields, water usage, nutrient uptake, and other important parameters. Crop modeling helps in understanding the complex interactions between different variables affecting crop growth and assists farmers, researchers, and policymakers in making informed decisions related to crop management, resource allocation, and risk assessment.
Role of AI in crop modeling: Artificial Intelligence (AI) plays a significant role in enhancing crop modeling by leveraging advanced computational techniques to improve model accuracy, efficiency, and scalability. One of the most important aspects of precision farming is sustainability. Using artificial neural networks (ANNs), a highly effective multilayer perceptron (MLP) model. The most common type in crop modeling is DSSAT , DSSAT (Decision Support System for Agro-technology Transfer).The Decision Support System for Agro-technology Transfer (DSSAT) is a software application program that comprises crop simulation models for over 42 crops (as of Version 4.8.2) as well as tools to facilitate effective use of the models. The tools include database management programs for soil, weather, crop management and experimental data, utilities, and application programs. The crop simulation models simulate growth, development and yield as a function of the soil-plant-atmosphere dynamics.DSSAT and its crop simulation models have been used for a wide range of applications at different spatial and temporal scales. This includes on-farm and precision management, regional assessments of the impact of climate variability and climate change, gene-based modeling and breeding selection, water use, greenhouse gas emissions, and long-term sustainability through the soil organic carbon and nitrogen balances.In conclusion, crop modeling stands as a crucial tool in modern agriculture, offering a systematic approach to understanding and predicting crop growth dynamics in diverse environmental conditions. By simulating the complex interactions between various factors influencing crop development, including climate, soil properties, agronomic practices, and genetic traits, crop models provide valuable insights for farmers, researchers, and policymakers.
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.
Crop yield prediction using data mining techniques.pdfssuserb22f5a
Agriculture is the main source of occupation which forms the backbone of our country. It involves the production of crops which may be either food crops or commercial crops. The productivity of crop yield is significantly influenced by various parameters such as rainfall, farm capacity, temperature, crop population density, humidity, irrigation, fertilizer application, solar radiation, type of soil, depth, tillage and soil organic matter. An accurate crop yield prediction support decision-makers in the agriculture sector to predict the yield effectively. Machine learning techniques and deep learning techniques play a significant role in the analysis of data for crop yield prediction. However, the selection of appropriate techniques from the pool of available techniques imposes challenges to the researchers concerning the chosen crop. In this paper, an analysis has been performed on various deep learning and machine learning techniques. To know the limitations of each technique, a comparative analysis is carried out in this paper. In addition to this, a suggestion is provided to further improve the performance of crop yield prediction.
CROP SIMULATION MODELS AND THEIR APPLICATIONS IN CROP PRODUCTION.pptxSarthakMoharana
CROP SIMULATION MODELS AND THEIR APPLICATIONS IN CROP PRODUCTION
Crop growth is a very complex phenomenon and a product of a series of complicated interactions of soil, plant and weather.
Crop growth simulation is a relatively recent technique that facilitates quantitative understanding of the effects of these factors and agronomic management factors on crop growth and productivity.
These models are quantitative description of the mechanisms and processes that result in growth of crop. The processes could be physiological, physical and chemical processes of crop.
MAJOR & POPULAR CROP SIMULATION MODELS:
DSSAT (Decision Support System for Agrotechnology Transfer)
Aqua Crop
Info Crop
APSIM (Agricultural Production System Simulator
ISSN 2321 – 9602
It seems like you're describing the publication process of a journal or publication called . This information provides insight into the journal's commitment to a fast publication schedule while maintaining rigorous peer review of the journalism research paper.
A poultry yield prediction model have then designed using a data mining and machine learning technique called Classification and Regression Tree (CART) algorithm. The developed model has been optimized and pruned using the Reduced Error Pruning (REP) algorithm to improve prediction accuracy. An algorithm to make the prediction model flexible and capable of making predictions irrespective of poultry size or population has been proposed. The model can be used by poultry farmers to predict yield even before a breeding season. The model can also be used to help farmers take decisions to ensure desirable yield at the end of the breeding season.
These days we have an increased number of heart diseases including increased risk of heart attacks. Our proposed system users sensors that allow to detect heart rate of a person using heartbeat sensing even if the person is at home. The sensor is then interfaced to a microcontroller that allows checking heart rate readings and transmitting them over internet. The user may set the high as well as low levels of heart beat limit. After setting these limits, the system starts monitoring and as soon as patient heart beat goes above a certain limit, the system sends an alert to the controller which then transmits this over the internet and alerts the doctors as well as concerned users. Also the system alerts for lower heartbeats. Whenever the user logs on for monitoring, the system also displays the live heart rate of the patient. Thus concerned ones may monitor heart rate as well get an alert of heart attack to the patient immediately from anywhere and the person can be saved on time.This value will continue to grow if no proper solution is found. Internet of Things (IoT) technology developments allows humans to control a variety of high-tech equipment in our daily lives. One of these is the ease of checking health using gadgets, either a phone, tablet or laptop. we mainly focused on the safety measures for both driver and vehicle by using three types of sensors: Heartbeat sensor, Traffic light sensor and Level sensor. Heartbeat sensor is used to monitor heartbeat rate of the driver constantly and prevents from the accidents by controlling through IOT.
ABSTRACT The success of the cloud computing paradigm is due to its on-demand, self-service, and pay-by-use nature. Public key encryption with keyword search applies only to the certain circumstances that keyword cipher text can only be retrieved by a specific user and only supports single-keyword matching. In the existing searchable encryption schemes, either the communication mode is one-to-one, or only single-keyword search is supported. This paper proposes a searchable encryption that is based on attributes and supports multi-keyword search. Searchable encryption is a primitive, which not only protects data privacy of data owners but also enables data users to search over the encrypted data. Most existing searchable encryption schemes are in the single-user setting. There are only few schemes in the multiple data users setting, i.e., encrypted data sharing. Among these schemes, most of the early techniques depend on a trusted third party with interactive search protocols or need cumbersome key management. To remedy the defects, the most recent approaches borrow ideas from attribute-based encryption to enable attribute-based keyword search (ABKS
Cloud computing is the one of the emerging techniques to process the big data. Large collection of set or large
volume of data is known as big data. Processing of big data (MRI images and DICOM images) normally takes
more time compare with other data. The main tasks such as handling big data can be solved by using the concepts
of hadoop. Enhancing the hadoop concept it will help the user to process the large set of images or data. The
Advanced Hadoop Distributed File System (AHDF) and MapReduce are the two default main functions which
are used to enhance hadoop. HDF method is a hadoop file storing system, which is used for storing and retrieving
the data. MapReduce is the combinations of two functions namely maps and reduce. Map is the process of
splitting the inputs and reduce is the process of integrating the output of map’s input. Recently, in medical fields
the experienced problems like machine failure and fault tolerance while processing the result for the scanned
data. A unique optimized time scheduling algorithm, called Advanced Dynamic Handover Reduce Function
(ADHRF) algorithm is introduced in the reduce function. Enhancement of hadoop and cloud introduction of
ADHRF helps to overcome the processing risks, to get optimized result with less waiting time and reduction in
error percentage of the output image
Text mining has turned out to be one of the in vogue handle that has been joined in a few research
fields, for example, computational etymology, Information Retrieval (IR) and data mining. Natural
Language Processing (NLP) methods were utilized to extricate learning from the textual text that is
composed by people. Text mining peruses an unstructured form of data to give important
information designs in a most brief day and age. Long range interpersonal communication locales
are an awesome wellspring of correspondence as the vast majority of the general population in this
day and age utilize these destinations in their everyday lives to keep associated with each other. It
turns into a typical practice to not compose a sentence with remedy punctuation and spelling. This
training may prompt various types of ambiguities like lexical, syntactic, and semantic and because of
this kind of indistinct data; it is elusive out the genuine data arrange. As needs be, we are directing
an examination with the point of searching for various text mining techniques to get different
textual requests via web-based networking media sites. This review expects to depict how
contemplates in online networking have utilized text investigation and text mining methods to
identify the key topics in the data. This study concentrated on examining the text mining
contemplates identified with Facebook and Twitter; the two prevailing web-based social networking
on the planet. Aftereffects of this overview can fill in as the baselines for future text mining research.
Colorectal cancer (CRC) has potential to spread within the peritoneal cavity, and this transcoelomic
dissemination is termed “peritoneal metastases” (PM).The aim of this article was to summarise the current
evidence regarding CRC patients at high risk of PM. Colorectal cancer is the second most common cause of cancer
death in the UK. Prompt investigation of suspicious symptoms is important, but there is increasing evidence that
screening for the disease can produce significant reductions in mortality.High quality surgery is of paramount
importance in achieving good outcomes, particularly in rectal cancer, but adjuvant radiotherapy and chemotherapy
have important parts to play. The treatment of advanced disease is still essentially palliative, although surgery for
limited hepatic metastases may be curative in a small proportion of patients.
Heat transfer in pipes is a distinctive kind of procedure employed in heat exchanger which transfers great
deal of heat because of the impact of capillary action and phase change heat transfer principle. Late improvement
in the heat pipe incorporates high thermal conductivity liquids like Nano liquids, fixed inside to extricate the most
extreme heat. This paper audits, impact of different factors, for example, thermal pipe tilt edge, charged measure
of working liquid, nano particles sort, size, and mass/volume part and its impact on the change of thermal
proficiency, thermal exchange limit and decrease in thermal protection. The Nano liquid arrangement and the
examination of its thermal attributes likewise have been investigated. The retained sun oriented vitality is
exchanged to the working liquid streaming in the pipe. The execution of the framework is affected by thermal
exchange from tube to working liquid, with least convective misfortunes, which must be considered as one of the
essential plan factor. In tube and channel streams, to improve the rate of heat exchange to the working liquid,
detached enlargement methods, for example, contorted tapes and swirl generators are employed from the fluid
flow path. The variation of heat transfer coefficient and pressure drop in the pipe flow for water and water based
Al2O3 Nano fluids at different volume concentrations and twisted tapes are studied.
Now-a-day’s pedal powered grinding machine is used only for grinding purpose. Also, it requires lots of efforts
and limited for single application use. Another problem in existing model is that it consumed more time and also has
lower efficiency. Our aim is to design a human powered grinding machine which can also be used for many purposes
like pumping, grinding, washing, cutting, etc. it can carry water to a height 8 meter and produces 4 ampere of electricity
in most effective way. The system is also useful for the health conscious work out purpose. The purpose of this technical
study is to increase the performance and output capacity of pedal powered grinding machine.
This project proposes a distributed control approach to coordinate multiple energy storage units
(ESUs) to avoid violation of voltage and network load constraints ESU as a buffer can be a promising
solution which can store surplus power during the peak generation periods and use it in peak load
periods.In ESU converters both active and reactive power are used to deal with the power quality
issues in distribution network ESU’s reactive power is proposed to be used for voltage support, while
the active power is to be utilized in managing network loading.
The steady increase in non-linear loads on the power supply network such as, AC variable speed drives,
DC variable Speed drives, UPS, Inverter and SMPS raises issues about power quality and reliability. In this
subject, attention has been focused on harmonics . Harmonics overload the power system network and cause
reliability problems on equipment and system and also waste energy. Passive and active harmonic filters are
used to mitigate harmonic problems. The use of both active and passive filter is justified to mitigate the
harmonics. The difficulty for practicing engineers is to select and deploy correct harmonic filters , This paper
explains which solutions are suitable when it comes to choosing active and passive harmonic filters and also
explains the mistakes need to be avoided.
This Paper is aimed at analyzing the few important Power System equipment failures generally
occurring in the Industrial Power Distribution system. Many such general problems if not resolved it may
lead to huge production stoppage and unforeseen equipment damages. We can improve the reliability of
Power system by simply applying the problem solving tool for every case study and finding out the root cause
of the problem, validation of root cause and elimination by corrective measures. This problem solving
approach to be practiced by every day to improve the power system reliability. This paper will throw the light
and will be a guide for the Practicing Electrical Engineers to find out the solution for every problem which
they come across in their day to day maintenance activity.
Today internet security is a serious problem. For every consumer and business that is on the Internet,
viruses, worms and crackers are a few security threats. There are the obvious tools that aid information security
professionals against these problems such as anti-virus software, firewalls and intrusion detection systems, but
these systems can only react to or prevent attacks-they cannot give us information about the attacker, the tools
used or even the methods employed. Given all of these security questions honeypots are a novel approach to
network security and security research alike. It is a resource, which is intended to be attacked and compromised to
gain more information about the attacker and the used tools. It can also be deployed to attract and divert an
attacker from their real targets. Honeypots is an additional layer of security. Honeypots have the big advantage that
they do not generate false alerts as each observed traffic is suspicious, because no productive components are
running on the system. The levels of interaction determines the amount of functionality a honeypots provides that
is low and high interactions.
More from IJET - International Journal of Engineering and Techniques (20)
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
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This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
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and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
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Vaccine management system project report documentation..pdfKamal Acharya
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In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
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Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
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1. International Journal of Engineering and Techniques - Volume 5 Issue 6, December 2019
Analysis of Crop Phenology-a Component of Parallel Agriculture
Management System using IOT
Mohini Avatade
Computer Engineering
Dr.D.Y.Patil Institute of Engineering Management and
Research,Akurdi
Pune,India
monu13.engg@gmail.com
Shreeya Murtadak
Computer Engineering
Dr.D.Y.Patil Institute of Engineering Management and
Research,Akurdi
Pune,India
shreeya9896@gmail.com
Komal Thutte
Computer Engineering
Dr.D.Y.Patil Institute of Engineering Management and
Research,Akurdiline 4: Pune,India
komalthutte1@gmail.com
Swati Totare
Computer Engineering
Dr.D.Y.Patil Institute of Engineering management and
Research,Akurdi
Pune,India
totareswati5@gmail.com
2. Abstract— To study crop recommendation fertilization as
per the weather condition of the rural farmers as the
main in our country, the paper took towns and villages of
Hua County as the study area, took recommendation
fertilization of wheat, maize and peanut as the study
object, designed model components of crop balance
fertilization by using Object-Oriented technique, and
developed the decision-making system about crop
recommendation fertilization based on ArcGIS Server at
village scale. The decision-making system realized
farmland nutrient management and fertilization
recommendations decision-making according to soil
output capacity, agricultural production level and crop
target yield. It was successfully applied in crop
production in Hua County. The research results show
that the system has the characteristic of better
expansibility than before, and it is significantly simple
and practical to reduce crop production cost and increase
agricultural production efficiency, which provides
technical support for crop fertilization decision-making
and is significant to improve agricultural ecological
environment and increase the comprehensive production
capacity of farmland.
I. INTRODUCTION
India is one among the oldest countries which is still
practicing agriculture. But in recent times the trends in
agriculture has drastically evolved due to globalization.
Various factors have affected the health of agriculture in
India. Many new technologies have been evolved to regain
the health. One such technique is precision agriculture.
Precision agriculture is budding in India. Precision
agriculture is the technology of “site-specific” farming. It
has provided us with the advantage of efficient input, output
and better decisions regarding farming. Although precision
agriculture has delivered better improvements it is still
facing certain issues. There exist many systems which
propose the inputs for a particular farming land. Propose
crops, fertilizers and even farming techniques.
Recommendation of crops is one major domain in precision
agriculture. Recommendation of crops is dependent on
various parameters. Precision agriculture aims in
identifying these parameters in a site-specific manner in
order to resolve issues regarding crop selection.
This set is usually referred to as a training set,
because, in general, it is used to train the classification
technique how to perform its classification. The
classification task can be seen as a supervised technique
where each instance belongs to a class, which is indicated
by the value of a special goal attribute or simply the class
attributes. Classification routines with data mining use a
variety of algorithms and the particular algorithm used can
affect the way records are classified. This work talks about
Decision Tree classifier assumes that the presence (or
absence) of a particular feature of a class is unrelated to the
presence (or absence) of any other feature. Depending on
the precise nature of the probability model, K Nearest
Neighbor (KNN) and Density based clustering can be
trained very efficiently in a supervised learning setting.
II. Literature Survey
1. Rupanjali D. Baruah, Sudipta Roy, R.M. Bhagat, L.N.
Sethi “Use of Data Mining Technique for Prediction of
Tea Yield in the Face of Climate Change of Assam,
India”, 2016 International Conference on Information
Technology.
Data mining is an emerging field of research in
Information Technology as well as in agriculture. The
present study focus on the applications of data mining
techniques in tea plantations in the face of climatic
change to help the farmer in taking decision for farming
and achieving the expected economic return. This paper
presents an analysis using data mining techniques for
estimating the future yield prediction in tea cultivation
with climatic change trends observed in last 30 years
(1977-2006). The patterns of crop production in response
to the climatic (rainfall, temperature, relative humidity,
evaporation and sunshine) effect across the four tea
growing regions (South Bank, North Bank, Upper Assam
and Cachar) of Assam were developed using Multiple
Linear Regression (MLR) technique. The tea production
3. estimation equations developed for the regions were
validated for the future yield prediction (2007, 2009 and
2010) and were found to be significant. Thus it is
suggested that the planters/farmers could use the
technique to predict the future crop productivity and
consequently adopt alternative adaptive measures to
maximise yield if the
predictions fall below expectations and commercial
viability.
2. Gregory S. McMaster, DA Edmunds, W.W. Wilhelm ,l,
D.C. Nielsen, P.v.v. Prasad.c. Ascough,
“PhenologyMMS: A program to simulate crop
phonological responses to water stress ”Journal
Computers and Electronics in Agriculture 77 (2011) 118-
125.
Crop phenology is fundamental for understanding
crop growth and development, and increasingly
influences many agricultural management practices.
Water deficits are one environmental factor that can
influence crop phenology through shortening or
lengthening the developmental phase, yet the
phonological responses to water deficits have rarely been
quantified. The objective of this paper is to provide an
overview of a decision support technology software tool,
PhenologyMMS Vl.2, developed to simulate the
phenology of various crops for varying levels of soil
water. The program is intended to be simple to use,
requires minimal information for calibration, and can be
incorporated into other crop simulation models. It
consists of a Java interface connected to FORTRAN
science modules to simulate phonological responses. The
complete developmental sequence of the shoot apex
correlated with phonological events, and the response to
soil water availability for winter and spring wheat
(Triticum aestivum L.), winter and spring barley
(Hordeum vulgare L.), corn (Zea mays L.), sorghum
(Sorghum bicolor L.), proso millet (Panicum milaceum
L.), hay/foxtail millet [Setaria italica (L.) P. Beauv.]. and
sunflower (Helianthus annus L.) were created based on
experimental data and the literature. Model evaluation
consisted of testing algorithms using “generic” default
phenology parameters for wheat (i.e., no calibration for
specific cultivars was used) for a variety of field
experiments to predict developmental events. Results
demonstrated that the program has general applicability
for predicting 3. Bruno Basso, Davide Cammarano,
Elisabetta Carfagna, Review of Crop Yield
Forecasting Methods and Early Warning Systems”,
Journal of convergence in engineering, technology and
science, Vol.1,pp.1-8,2009.
The following review paper presents an overview of the
current crop yield forecasting methods and early warning
systems for the global strategy to improve agricultural
and rural statistics across the globe. Different sections
describing simulation models, remote sensing, yield gap
analysis, and methods to yield forecasting compose the
manuscript.
4. Young Ju Jeong, Kwang Eun An, Sung Won Lee, and
Dongmahn Seo, “Improved Durability of Soil Humidity
Sensor for Agricultural IoT Environments”, 2018 IEEE
International Conference on Consumer Electronics
(ICCE).
Soil humidity is the most important factor for plant
growth. Therefore, the soil humidity sensor is an
important part of smart farm application using
agricultural IoT environments. Since soil humidity
sensors are applied wet underground and the sensor
consists of copper, rust eats away the copper surface of
sensors. From rusting of sensors, wrong information of
soil humidity can be collected on smart farm system
based on agricultural IoT Environments. It makes that
smart farm is not reliable. In this paper, we propose a
new type of soil humidity sensor in order to extend life
time.
5. M. Trnka, M. Dubrovsky, D. Semeradova, and Z. Z
alud, Projections of uncertainties in climate change
scenarios into expected winter wheat yields”, in
Proceedings of the 11th European Conference on
Computer Vision: Part I, pp. 285298,2003.
4. The crop model CERES-Wheat in combination with
the stochastic weather generator were used to quantify
the effect of uncertainties in selected climate change
scenarios on the yields of winter wheat, which is the most
important European cereal crop. Seven experimental
sites with the high quality experimental data were
selected in order to evaluate the crop model and to carry
out the climate change impact analysis. The analysis was
based on the multiyear crop model simulations run with
the daily weather series prepared by the stochastic
weather generator. Seven global circulation models
(GCMs) were used to derive the climate change scenarios.
In addition, seven GCM-based scenarios were averaged
in order to derive the average scenario (AVG).
III. Proposed System
The datasets have been collected and refined based
on commonality uses such as soil moisture,
temperature, humidity, evaporation, rainfall,
sunshine. These data sets need to be entered into
the database .
From these parameters name of the crop and
predicted yield rate of the crop can be predicted.
Past dataset is used as training data and the data
which will be obtained using sensors will be used as
testing data.
Multiple Linear Regression model will be created
using Training data. For testing, using sensors
values, soil moisture, temperature, humidity,
evaporation, rainfall, sunshine are measured and
taken as input with the help of weather forecasting
department.
By analyzing and predicting, the crop name and
approximate yield rate of particular crop can be
found out. This helps the farmers to take the correct
decision to sow the crops such that yield rate can be
increased.
IV. ENTITY RELATIONSHIP DIAGRAM
5. V. CONCLUSION
This system focuses on developing automated leaf diseases.
It saves time and effort, In this project, we have proposed a new
method for prediction of crop disease from current weather using
Google API with the help of K-NN algorithm and measuring the
crop diseases of the crop object and find weather prediction. In
this work the experiments are performed two important and well
known classification algorithms K-Nearest Neighbor (K-NN) and
Density based clustering are applied to the dataset. There
accuracy is obtained by evaluating the datasets. Each algorithm
has been run over the training dataset and their performance in
terms of accuracy is evaluated along with the prediction done in
the testing dataset. The entire analysis process creates a data flow.
VI. REFERENCES
[1] Adams, R., Fleming, R., Chang, C., McCarl, B., and
Rosenzweig, 1993 ―A Reassessment of the Economic
Effects of Global Climate Change on U.S. Agriculture,
Unpublished: September.
[2] Adams, R., Glyer, D., and McCarl, B. 1989. "The
Economic Effects of Climate Change on U. S. Agriculture: A
Preliminary Assessment." In Smith, J., and Tirpak, D.,eds.,
The Potential Effects of Global Climate Change onthe
United States. Washington, D.C.: USEPA.
[3] Adams, R.,Rosenzweig, C., Peart, R., Ritchie, J.,
McCarl,B., Glyer, D., Curry, B., Jones, J., Boote, K., and
Allen, H.1990."Global Climate Change and U. S.
Agriculture."Nature.345 (6272, May): 219-224.
[4] Adaptation to Climate Change Issues of Longrun
Sustainability." An Economic Research
[5] Barron, E. J. 1995."Advances in Predicting Global
Warming‖. The Bridge (National Academy of Engineering).
25 (2, Summer): 10-15.
[6] Barua, D. N. 2008. Science and Practice in Tea
Culture,second ed. Tea Research Association, Calcutta-
Jorhat, India.
[7] Basu, Majumder, A., Bera, B. and Rajan, A. 2010.
Teastatistics: Global scenario. Int. J. Tea Sci.8: 121-124.
[8] Bazzaz, A., and Fajer, E. D. 1992. "Plant Life in a
CO2Rich World. "Scientific American. 1821.
[9] Brack, D. and M. Grubb. 1996. Climate Change,
"ASummary of the Second Assessment Report of the
IPCC."FEEM (Fondazione ENI Enrico Mattei, Milano
Italy) newsletter, 3, 1996
[10] M.Soundarya, R.Balakrishnan,” Survey on
Classification Techniques in Data mining”, International
Journal of Advanced Research in Computerand
Communication Engineering Vol. 3, Issue 7, July 2014.
[11] D Ramesh, B Vishnu Vardhan, “Data mining technique
and applications to agriculture yield data”, International
Journal of Advanced Researchin Computer and
Communication Engineering Vol. 2, Issue 9, September
2013.
[12] Gideon O Adeoye, Akinola A Agboola, “Critical levels
for soil pH, available P, K, Zn and Mn and maize ear-leaf
content of P, Cu and Mn insedimentary soils of South-
Western Nigeria”, Nutrient Cycling in Agroeco systems,
Volume 6, Issue 1, pp 65-71, February 1985.
[13] D. Almaliotis, D. Velemis, S. Bladenopoulou, N.
Karapetsas, “Appricot yield in relation to leaf nutrient
levels in Northern Greece”, ISHS ActaHorticulturae 701:
XII International Symposium on Apricot Culture and
Decline.