Digital Agriculture

Digital agriculture leverages data-driven technologies to optimize farming practices, aiming to increase yields, improve resource management, and enhance sustainability. Current research emphasizes the development and application of machine learning models, including Random Forests and 3D Vision Transformers, for tasks such as precision field boundary mapping using satellite imagery, dynamic management zone delineation, and crop yield prediction. This field is significant for its potential to address global food security challenges, improve farm efficiency, and promote environmentally conscious agricultural practices through data-centric approaches and the development of explainable AI frameworks.