High-precision crop recommendation system with stacking ensemble classifiers for optimizing agricultural productivity.

Crop productivity is crucial for farmers and economy worldwide. Factors such as fertilization, weather, and climate have a significant impact on yields. To improve crop productivity, a crop recommendation system is introduced in this paper. It provides data-driven advice on the best crops to plant, taking into account climate, weather, and soil nutrients. This research work introduces feature fusion with a stacking ensemble model comprising 18 classifiers and three novel methods to enhance crop recommendation and mitigate overfitting compared to other ensemble techniques. In this paper, we also examine two datasets for model validation; one of them is a large dataset containing nearly 28,242 records. The findings of our study reveal that feature fusion enables all ensemble classifiers to not only exceed the accuracy and precision of other established modern techniques, but also reduce overfitting, especially for the three proposed models that depend on a large dataset. In our experiments, the accuracy of ensemble models in categorizing diverse crops under different conditions ranges from 98.4% to 99.54%. Notably, the voting ensemble classifier proved to be the most effective, when applied to the first small dataset, achieving an impressive accuracy up to 99.56%. The second stacking ensemble classifier proved to be the most effective, when applied to the second large dataset, achieving an accuracy up to 85.6%.