Review of AI methods in precision agriculture

This review analyzes the increasing adoption of Artificial Intelligence (AI) in precision agriculture, paying special attention to the advances in crop management brought by machine learning and deep learning technologies. From scouting to pest and disease detection, weeding, irrigation, and crop quality estimation, tasks traditionally plagued by human error and excessive manual work are now being addressed by AI solutions which are quicker, precise, and easily scalable. This review also examines the use of drones and sensors integrated with the Internet of Things and robotics, alongside real-time monitoring, predictive analytics, and automated decision-making, the foreseen and observable enhancements of AI in agriculture, particularly in reducing chemical use and improving efficiency alongside AI techniques such as Support Vector Machines, Random Forest, Convolutional Neural Networks, and Vision and Hybrid Transformers. Nonetheless, there are still significant challenges such as the high computational demands and limited availability of large high-quality datasets, the expense to smallholder farmers, and privacy concerns. We believe that AI specialists and agricultural scientists collaborating on affordable, reliable, and field-ready innovations would have the greatest impact on stimulating widespread adoption. In essence, the review reinforces the idea that AI technologies can boost the resiliency, productivity, and sustainability of agriculture.