JoWUA

Abstract

Precision agriculture is recognized as one of the essential practices for maximizing crop yield and optimizing resource utilization in agricultural practices. Deep learning algorithms, combined with high-quality sensor data, can extract useful information to make accurate predictions for crop yield on time. Because deep learning methods are adept at identifying patterns in large datasets, it's beneficial to apply them to projects such as predicting crop yield. Deep-learning models identify the intricate relationships between these variables and crop yield by leveraging data from sources such as weather reports, soil information, and crop health data. Finally, the deep learning model integrates with a portion of the optimized sensor data to enhance the precision of crop yield prediction. The sensors collect precise, in situ measurements of soil moisture and nutrients.