Enhancing Crop Prediction Accuracy Via Soil and Environmental Feature Selection Techniques in Machine Learning

Historically, farmers' practical knowledge was a significant factor in agricultural decisions. The start of climate change has, nevertheless, harmed crop output. Consequently, farmers face challenges when choosing the optimal crops considering the soil and environmental elements. Traditional manual methods of predicting the best crops to grow on particular types of terrain have typically yielded unsatisfactory results. Acquiring accuracy in crop forecasts is essential for increasing agricultural productivity. It is the precise point in the crop forecast process that machine learning plays a significant role. Crop forecast accuracy depends on several factors, including soil type, location, and regional weather variations. A fundamental aspect of prognostication is carefully identifying important qualities matched to particular crops, supported by feature selection methods. The current study orchestrates a comparison of several selection strategies for wrapper features. The experimental results show that the Recursive Feature Elimination method combined with the Adaptive Bagging classifier performs better than the alternative approaches. These procedures, which fall under the category of crop prediction and use categorization techniques, not only pinpoint the appropriate crops for specific plots of land but also plot the route for future development.