A Machine Learning Approach to Identify Optimal Cultivation Practices for Sustainable apple Production in Precision Agriculture in Morocco

Abstract

Precision agriculture techniques have been increasingly adopted worldwide to optimize cultivation practices and achieve sustainable crop production. In this study, we developed a Machine Learning approach to identify optimal cultivation practices for sustainable apple production in precision agriculture in the Msemrir town Morocco. We collected a dataset of cultivation practices and apple yield and size data from 10 farms in the town and used correlation-based feature selection and three Machine Learning algorithms (Linear Regression, Decision Tree, and Random Forest) to develop predictive models. The results showed that irrigation, fertilization, and pruning are the most important cultivation practices for apple production in the region, and the Random Forest model performed the best in predicting apple yield and size based on the selected practices. The use of Machine Learning techniques can help farmers optimize cultivation practices and achieve sustainable apple production by reducing inputs such as water and fertilizer and minimizing environmental impact. Moreover, the use of precision agriculture techniques can help farmers meet consumer demand for sustainable and high-quality apple products.