Prediction of Agriculture Yieldsusing Machine Learning Algorithm

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Jagannath Ray, Rajesh Kumar Ojha, Surabika Hota


In recent years, great efforts have been carried out on the challenging task of predicting
different crop yields. Developing exact models for crop yield estimation utilizing Information and
Communication Technologies may support farmers and different stakeholders to improve decision
making about national food import/export and food security. Most of the crops are selected based on
the economic range. In our proposed work also we have consider the economical crops and they
provide better prediction compared with the existing classifiers. The proposed ensemble classifier
provides an efficient crop yield and crop disease forecasting model. Our proposed work provides
knowledge to the farmers about the climatic conditions of the probability of crop disease and the
climatic conditions for better crop yield. Even it discovers the crop yield and crop diseases, but does
not concentrate on the solution to solve the productivity issue caused by crop diseases. Further, our
future work concentrates on the above issue with different algorithms.


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How to Cite
Jagannath Ray, Rajesh Kumar Ojha, Surabika Hota. (2022). Prediction of Agriculture Yieldsusing Machine Learning Algorithm. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 11(3), 1381–1391.
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