Maize yield prediction based on multi-source data using deeping learning algorithms

the research objectives are as follows: 1) To identify and collect multi-source data (field data, remote sensing data, meteorology data, soil data, and historical data) related to crop yield. 2) Develop biomass estimation models for maize over the entire crop life cycle based on the collected data using AI algorithms. 3) Create optimal AI algorithm to automatically find the spatial-temporal variation law of maize yield and environmental factors, and screen out the factors affecting maize yield over different growth stages. 4) Develop AI-driven spatiotemporal yield prediction algorithms with high precision and spatio-temporal transferability.