Precision Agriculture using Machine Learning and Deep Learning Algorithms: A Comprehensive Study

Farming has evolved from the basic irrigation techniques used in ancient river valley civilizations to the sophisticated Precision Agriculture of today. It plays an important role in the advancement of human society. This paper explores the use of Machine Learning and Deep Learning algorithms in Precision Agriculture, an essential task in agriculture that helps ensure a stable food supply and improves the efficiency of food production. Despite advances in Precision Agriculture and the widespread adoption of Machine Learning and Deep Learning algorithms, a comprehensive review that systematically addresses the challenges of data quality, model interoperability, and multisource data integration in Precision Agriculture is still lacking. We aim to bridge this gap by analyzing more than 100 related studies. We focus on applying several Machine Learning and Deep Learning algorithms, such as Artificial Neural Networks, Support Vector Machines, Convolutional Neural Networks, Random Forests, etc. We use a comparative analysis methodology to identify key features influencing Precision Agriculture, such as temperature, rainfall, remote sensing data, soil types, etc. Our findings highlight continuous challenges in standardizing data protocols and developing Explainable AI models that can be generalized across diverse agricultural conditions. The key takeaway is that integrating IoT with real-time data processing can significantly improve agricultural resilience and efficiency. Future research should focus on refining robust models and expanding multisource data integration to address these challenges effectively.