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
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
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.
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
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.
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.
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.
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.
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 Prediction System using Machine Learningijtsrd
Indias economy is mostly based on agricultural yield growth and linked agro industry products, as it is an agricultural country. Rainwater, which is often unpredictable in India, has a significant impact on agriculture. Agriculture growth is also influenced by a variety of soil parameters, such as nitrogen, phosphorus, and potassium, as well as crop rotation, soil moisture, and surface temperature, as well as climatic factors such as temperature and rainfall. India is quickly advancing in terms of technical advancement. As a result, technology will benefit agriculture by increasing crop productivity, resulting in higher yields for farmers. The suggested project provides a solution for storing temperature, rainfall, and soil characteristics in order to determine which crops are suited for cultivation in a given area. This paper describes a system, implemented as an android application, that employs data analytics techniques to predict the most profitable crop based on current weather and soil conditions. The suggested system will combine data from a repository and the weather department using a machine learning algorithm Using Multiple Linear Regression, it is possible to anticipate the most suited crops based on current environmental circumstances. This gives a farmer a wide range of crops to choose from. As a result, the project creates a system that integrates data from diverse sources, performs data analytics, and conducts predictive analysis in order to improve crop production productivity and boost farmer profit margins over time. Machine learning, crop prediction, and yield estimation are some of the terms used in this paper. Manju D C | Murugan R "Crop Prediction System using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-3 , April 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49725.pdf Paper URL: https://www.ijtsrd.com/computer-science/data-processing/49725/crop-prediction-system-using-machine-learning/manju-d-c
Crop yield prediction using ridge regression.pdfssuserb22f5a
Crop yield prediction using deep neural networks with data mining concepts by applying multi model ensembles using ridge regression to increase accuracy, precision, recall,and f measure. Combining neural networks with regression increase high satisfactory crop yield prediction.the support vector regression is slow convergence , stuck in local minima. But ridge regression analyse multicollinearity in multiple regression.
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.
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.
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.
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 Prediction System using Machine Learningijtsrd
Indias economy is mostly based on agricultural yield growth and linked agro industry products, as it is an agricultural country. Rainwater, which is often unpredictable in India, has a significant impact on agriculture. Agriculture growth is also influenced by a variety of soil parameters, such as nitrogen, phosphorus, and potassium, as well as crop rotation, soil moisture, and surface temperature, as well as climatic factors such as temperature and rainfall. India is quickly advancing in terms of technical advancement. As a result, technology will benefit agriculture by increasing crop productivity, resulting in higher yields for farmers. The suggested project provides a solution for storing temperature, rainfall, and soil characteristics in order to determine which crops are suited for cultivation in a given area. This paper describes a system, implemented as an android application, that employs data analytics techniques to predict the most profitable crop based on current weather and soil conditions. The suggested system will combine data from a repository and the weather department using a machine learning algorithm Using Multiple Linear Regression, it is possible to anticipate the most suited crops based on current environmental circumstances. This gives a farmer a wide range of crops to choose from. As a result, the project creates a system that integrates data from diverse sources, performs data analytics, and conducts predictive analysis in order to improve crop production productivity and boost farmer profit margins over time. Machine learning, crop prediction, and yield estimation are some of the terms used in this paper. Manju D C | Murugan R "Crop Prediction System using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-3 , April 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49725.pdf Paper URL: https://www.ijtsrd.com/computer-science/data-processing/49725/crop-prediction-system-using-machine-learning/manju-d-c
Crop yield prediction using ridge regression.pdfssuserb22f5a
Crop yield prediction using deep neural networks with data mining concepts by applying multi model ensembles using ridge regression to increase accuracy, precision, recall,and f measure. Combining neural networks with regression increase high satisfactory crop yield prediction.the support vector regression is slow convergence , stuck in local minima. But ridge regression analyse multicollinearity in multiple regression.
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