Predictive analytics in agriculture

In order to forecast agricultural yields and suggest suitable crops, this chapter examines the use of machine learning techniques, including Random Forest (RF), Decision Trees, Support Vector Machines, and K-Nearest Neighbors (KNN). Predicting crop yields accurately is essential for enhancing food security and streamlining farming methods. The study shows that by utilizing crop traits, soil composition, and meteorological data, these systems improve predictions. Key findings show that KNN performs best in crop recommendation tasks with 98.18% accuracy, while the RF model obtains 96.67% accuracy. These findings demonstrate how machine learning may be used to address issues in sustainable agriculture.