A Predictive Approach to Improving Agricultural Productivity in Morocco through Crop Recommendations

Abstract

Agricultural productivity is a critical component of sustainable economic growth, particularly in developing countries. Morocco, with its vast agricultural potential, is in need of advanced technologies to optimize crop productivity. Precision farming is one such technology, which incorporates the use of artificial intelligence and machine learning to analyze data from various sources and make informed decisions about crop management. In this study, we propose a web-based crop recommendation system that leverages ML algorithms to predict the most suitable crop to harvest based on environmental factors such as soil nutrient levels, temperature, and precipitations. We evaluated the performance of five ML algorithms (Decision Tree, Naïve Bayes, Random Forest, Logistic Regression, and Support Vector Machine) and identified Random Forest as the best-performing algorithm. Despite the promising results, we faced several challenges, including limited availability of data and the need for field validation of the results. Nonetheless, our platform aims to provide free and open-source precision farming solutions to Moroccan farmers to improve agricultural productivity and contribute to sustainable economic growth in the country.