Prediction of land suitability for food crop types using classification algorithms

Abstract

Decision-making in the selection of crop types is often conducted using conventional approaches. It is relying on limited experience and knowledge without considering the latest data or information. This approach has the loss of opportunities to use crop types. The crop types are more suited to environmental conditions and market demand, and it inhibits the application of innovation in agriculture. Therefore, the use of information technology becomes crucial to enhance accuracy in determining land suitability and crop selection. This study recommends the Random Forest algorithms and AdaBoost due to their excellent performance across all metrics (AUC, CA, F1, Precision, Recall) on various dataset sizes with scores above 0.9, so it is the solution to predict land suitability for specific crop types. Furthermore, it enables farmers to maximize land potential and achieve optimal yields.