Prediction on field crops yield based on analysis of deep learning model

Iniyan Shanmugam, Jebakumar Rethnaraj, Srinivasan Rajendran, Senthilraja Manickam


Agriculture has a key role in the overall economic development of the country. Climate change, irregular rainfall, changes in the nutrient content of the soil, and other environmental changes are seen as a severe problem in crop yield prediction. Using deep learning (DL) models that incorporate multiple factors can be viewed as an essential strategy for attaining accurate and effective solutions to this issue. The crop yield can be predicted using yield data obtained from a historical source that includes information about the weather, soil nutrient content, soil type, the season in which the crop was grown, and its yield. In order to train the model and achieve high accuracy, a large set of data including multiple factors would be required. This research aims to forecast the yield of a certain crop using long short-term memory (LSTM) time series analysis and the information currently available. The data used to construct the models was obtained from a reputable source and contains correct numbers. Before growing a crop that has been sown on a piece of agricultural land, the yield prediction utilizing advanced methodologies can assist farmers predict the yield of a specific crop.


Crop yield prediction; Dataset; Linear regression; Long short-term memory; Time series analysis

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