Towards enhancing the performance of crop prediction system for precision agriculture using feature correlation square-based nearest neighbor classifier

Precision agriculture is an enactment of increasing the profitability of crop yields by means of efficient farming practices. In India, farmers can monitor the situations of their surroundings and the ecosystem using precision agriculture in a short period of time. Crop prediction is a critical mission for the decision-makers at the state and district level for speedy decision-making. Therefore, the design and development of an intelligent crop prediction system with high accuracy is a pressing necessity that can assist farmers in determining the crop for cultivation in their fields. In datasets related to farming, different factors like soil nutrients, temperature, humidity, and rainfall commonly depend on each other. A lacuna in the existing crop prediction system is that the correlation between crop features may not be considered, resulting in poor system performance in terms of accuracy. The correlation between features is important as it directly affects the performance of the prediction system. In this study, a Feature Correlation Square based Nearest Neighbor (FCSNN) approach is proposed which extracts the correlation between crop features and predicts the type of crop using the nearest neighbor approach. The proposed crop prediction system is trained and tested using a publicly available benchmark crop recommendation agriculture dataset. It is observed that the proposed approach outperforms the existing crop prediction systems constructed using base classifiers.