Crop Yield Prediction based on Indian Agriculture using Machine Learning

Nishant, Potnuru Sai; Venkat, Pinapa Sai; Avinash, B.; Jabber, Bhukya

doi:10.1109/incet49848.2020.9154036

Cited by 122 publications

(27 citation statements)

References 19 publications

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“…Attributes like apparentemperaturemin, apparenttemperaturemax, and precipintensitymax have been removed since it is highly correlated with attributes like temperaturemax, temperaturemin, and precipAccumulation. Features like day length and elevation are added since they play an important role in crop yield prediction ( Nishant et al, 2020 ). After data preprocessing techniques such as imputation of missing values, attribute elimination, and adding the new attributes, the dataset contains 12 attributes.…”

Section: Discussionmentioning

confidence: 99%

“…The accuracy of the proposed algorithm is comparatively better than the existing work proposed by Kaur et al , 2020. In the earlier literature ( Nishant et al, 2020 and Medar et al, 2019 ), yield prediction was done by accepting input parameters in the terminal and not in the web interface. The farmers don’t have knowledge and don’t know how to use the terminal.…”

Section: Discussionmentioning

confidence: 99%

“…Attributes such as State, and Date are removed because their inclusion would result in overfitting and a lack of generalization ( Gandhi et al, 2016 ; Mythra et al, 2018 ). Features like length of day and elevation plays an important role in crop yield prediction ( Nishant et al, 2020 ). These features are the derived features, it is not available in the original dataset.…”

Section: Methodsmentioning

confidence: 99%

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An intelligent decision support system for crop yield prediction using hybrid machine learning algorithms

Anbananthen

Subbiah²,

Deisy³

et al. 2021

F1000Res

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Background: In recent times, digitization is gaining importance in different domains of knowledge such as agriculture, medicine, recommendation platforms, the Internet of Things (IoT), and weather forecasting. In agriculture, crop yield estimation is essential for improving productivity and decision-making processes such as financial market forecasting, and addressing food security issues. The main objective of the article is to predict and improve the accuracy of crop yield forecasting using hybrid machine learning (ML) algorithms. Methods: This article proposes hybrid ML algorithms that use specialized ensembling methods such as stacked generalization, gradient boosting, random forest, and least absolute shrinkage and selection operator (LASSO) regression. Stacked generalization is a new model which learns how to best combine the predictions from two or more models trained on the dataset. To demonstrate the applications of the proposed algorithm, aerial-intel datasets from the github data science repository are used. Results: Based on the experimental results done on the agricultural data, the following observations have been made. The performance of the individual algorithm and hybrid ML algorithms are compared using cross-validation to identify the most promising performers for the agricultural dataset. The accuracy of random forest regressor, gradient boosted tree regression, and stacked generalization ensemble methods are 87.71%, 86.98%, and 88.89% respectively. Conclusions: The proposed stacked generalization ML algorithm statistically outperforms with an accuracy of 88.89% and hence demonstrates that the proposed approach is an effective algorithm for predicting crop yield. The system also gives fast and accurate responses to the farmers.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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An intelligent decision support system for crop yield prediction using hybrid machine learning algorithms

Anbananthen

Subbiah²,

Deisy³

et al. 2021

F1000Res

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“…Most researchers have focused on developing traditional and advanced regression models in linear and nonlinear situations. Starting with the traditional multiple linear regression to predict the crop yield in Andhra Pradesh [2], kernel ridge, lasso, and elastic net regression models considering parameters such as the state, district, season, area, and year have been used to estimate the particular crop yield in India [3].…”

Section: Review Of the Literaturementioning

confidence: 99%

Prediction of Rice Cultivation in India—Support Vector Regression Approach with Various Kernels for Non-Linear Patterns

et al. 2021

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The prediction of rice yields plays a major role in reducing food security problems in India and also suggests that government agencies manage the over or under situations of production. Advanced machine learning techniques are playing a vital role in the accurate prediction of rice yields in dealing with nonlinear complex situations instead of traditional statistical methods. In the present study, the researchers made an attempt to predict the rice yield through support vector regression (SVR) models with various kernels (linear, polynomial, and radial basis function) for India overall and the top five rice producing states by considering influence parameters, such as the area under cultivation and production, as independent variables for the years 1962–2018. The best-fitted models were chosen based on the cross-validation and hyperparameter optimization of various kernel parameters. The root-mean-square error (RMSE) and mean absolute error (MAE) were calculated for the training and testing datasets. The results revealed that SVR with various kernels fitted to India overall, as well as the major rice producing states, would explore the nonlinear patterns to understand the precise situations of yield prediction. This study will be helpful for farmers as well as the central and state governments for estimating rice yield in advance with optimal resources.

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“…Nishant et al [22] proposed web application to forecast the yield of nearly all types of crops grown in India. This study is unique because it uses simple parameters such as state, district, season, and region to predict crop yields in any year the user desires.…”

Section: Related Workmentioning

confidence: 99%

Comparative Study for Classification Algorithms Performance in Crop Yields Prediction Systems

Ismael

Abdulazeez

Hasan

2021

QAJ

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The agriculture importance is not restricted to our daily life; it is also an effective field that enhances the economic growth in any country. Therefore, developing the quality of the crop yields using recent technologies is a crucial procedure to obtain competitive crops. Nowadays, data mining is an emerging research field in agriculture especially in the predicting and analysis of crop yield. This paper focuses on utilizing various data mining classification algorithms to predict the impact of various parameters such as area, season and production on the crop yield quality. The performance of the decision tree, naive Bayes, random forest, support vector machine and K-nearest neighbour is measured and compared to each other. The comparison involves measuring the error values and accuracy. The SVM algorithm achieved the highest accuracy value with 76.82%. while the lowest is achieved by the KNN algorithm with 35.76%. The highest error value was 111.8855 for KNN. Also, the prediction help farmer to increased and improved the income level.

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Crop Yield Prediction based on Indian Agriculture using Machine Learning

Cited by 122 publications

References 19 publications

An intelligent decision support system for crop yield prediction using hybrid machine learning algorithms

An intelligent decision support system for crop yield prediction using hybrid machine learning algorithms

Prediction of Rice Cultivation in India—Support Vector Regression Approach with Various Kernels for Non-Linear Patterns

Comparative Study for Classification Algorithms Performance in Crop Yields Prediction Systems

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