Crop Recommendation System Using Machine Learning and IoT: A Survey

The primary purpose of this review is to evaluate the effectiveness of machine learning (ML) algorithms in conjunction with IoT technologies in the development of advanced crop recommendation systems, which are aimed at enhancing the sustainability and productivity of India’s agricultural sector under climate change. Use of IoT devices has made it possible for continuous soil moisture and nutrient level monitoring, thereby improving the accuracy of recommendations. This review provides a fresh perspective on how ML and IoT can be merged toward crop recommendations by comparing methods, parameters, and outcomes used by different authors; something that previous reviews have often ignored. The study therefore highlights multidisciplinary approaches that integrate agriculture, socio-economics, and information technology to foster sustainable agriculture for posterity. By combining IoT with crop recommendations, we can combine the predictive capabilities of ML with the real-time data collection provided by IoT devices to improve and ensure agricultural yields. Together, these allow continuous monitoring of soil water content, nutrient levels, and climate to provide precisely tailored recommendations for crops best suited to a particular soil, highlighting the need for different approaches emphasizing interdisciplinary approaches that integrate social and economic, thus ensuring the future of sustainable agriculture.