Exploration of Machine Learning Approaches for Paddy Yield Prediction in Eastern Part of Tamilnadu

Abstract

Agriculture is the principal basis of livelihood that acts as a mainstay of any country. There are several changes faced by the farmers due to various factors such as water shortage, undefined price owing to demand–supply, weather uncertainties, and inaccurate crop prediction. The prediction of crop yield, notably paddy yield, is an intricate assignment owing to its dependency on several factors such as crop genotype, environmental factors, management practices, and their interactions. Researchers are used to predicting the paddy yield using statistical approaches, but they failed to attain higher accuracy due to several factors. Therefore, machine learning methods such as support vector regression (SVR), general regression neural networks (GRNNs), radial basis functional neural networks (RBFNNs), and back-propagation neural networks (BPNNs) are demonstrated to predict the paddy yield accurately for the Cauvery Delta Zone (CDZ), which lies in the eastern part of Tamil Nadu, South India. The performance of each developed model is examined using assessment metrics such as coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), coefficient of variance (CV), and normalized mean squared error (NMSE). The observed results show that the GRNN algorithm delivers superior evaluation metrics such as R2, RMSE, MAE, MSE, MAPE, CV, and NSME values about 0.9863, 0.2295 and 0.1290, 0.0526, 1.3439, 0.0255, and 0.0136, respectively, which ensures accurate crop yield prediction compared with other methods. Finally, the performance of the GRNN model is compared with other available models from several studies in the literature, and it is found to be high while comparing the prediction accuracy using evaluation metrics.

Crop yield forecasting by long short‐term memory network with Adam optimizer and Huber loss function in Andhra Pradesh, India

“…In the agricultural domain, crop yield prediction is a challenging task, where many research works are performed to predict better crop yield utilizing machine learning techniques 36–38 . In this article, the data was collected from the Telangana Agricultural Report (2017–2018) and preprocessed before being applied to classification.…”

“…In the agricultural domain, crop yield prediction is a challenging task, where many research works are performed to predict better crop yield utilizing machine learning techniques. [36][37][38] In this article, the data was collected from the Telangana Agricultural Report (2017-2018) and preprocessed before being applied to classification. The proposed model includes three phases such as data collection: real-time data, data preprocessing MMN, and classification: LSTM with Adam optimizer and Huber loss function.…”

Crop Yield Prediction Using Machine Learning Approaches on a Wide Spectrum

“…The BPNN tries to minimize the error function in weight space using the delta rule or gradient descent. The weights that minimize the error function to a global optimum are considered a solution to the learning problem [28].…”

“…According to SVM theory, the fitting problem can be derived as follows [28]: The ranges of a i, a * i , b are obtained through second optimization problems. Generally, a small portion of a i, a * i should not be zero and named as a support vector.…”

Comparison between Different Mustard Yield Prediction Models Developed using Various Techniques for Udaipur Region of Rajasthan

“…Crop simulation model based on weather provides detail description regarding impact of weather on crop yield on various growth stages and plays a vital role in providing area specific yield forecast under local weather condition. Joshua et al, [5] also emphasized on highlighting the importance of evaluating the performance and accuracy of the developed model using different metrics like RMSE, MAE and MAPE.…”