(untitled) (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2022)
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.