Optimizing Crop Yield Prediction and Disease Detection in Precision Agriculture Using Machine Learning Techniques

Optimizing crop yield while minimizing resource consumption is the goal of precision agriculture, a farming approach. The ability to accurately forecast crop yields and identify illnesses at an early stage is a crucial component of precision agriculture. The employment of machine learning methods to improve existing procedures is the main topic of this article. Effective agricultural output prediction and disease detection are both made possible by machine learning algorithms that sift through mountains of data acquired from sources like sensors and satellite photography. In order to make crop management procedures more efficient and accurate, this study examines existing approaches, finds problems, and suggests alternatives. Data sources for disease diagnosis and agricultural production prediction, the use of artificial intelligence algorithms, implementation obstacles, and future research objectives are important subjects. Farmers may maximize yields, minimize resource waste, and lessen crop losses caused by diseases by using artificial intelligence in precision agriculture. At the end of the day, this helps with food security and sustainable farming methods