Classification and Estimation of Crop Yield Prediction in Karnataka using LSTM with Attention Mechanism - Geddlehally Renukaradya, Nandini; Rao, Kishore Gopala; Jayachandra, Anand Babu (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Agriculture is an important occupation across the world with the dependency on the weather and rainfall. The objective of this paper is an early prediction of crop yield by using the climate, soil, and temperature factors. In this research, the classification-based crop yield prediction is proposed by using the Long Short-Term Memory (LSTM) with Attention Mechanism. The manual data is collected from the Economics and Statistics, Government of Karnataka department. This method utilized the dataset from the Department of Economics and Statistics of three crops named jowar, paddy, and ragi. The linear interpolation method is utilized for filling the missing and null values in the dataset. The feature selection process helps in the Correlation based Feature Selection Algorithm (CBFA) and Variance Inflation Factor Algorithm (VIF) for selecting and removing correlated feature sets. The model performance is evaluated by using Accuracy, R2, Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The proposed LSTM model delivers the results through evaluation metrics such as accuracy, R2, MAE, MSE, and RMSE values about 98.23%, 0.43, 0.131, 0.054 and 0.232 respectively.