Abstract
Artificial intelligence is critical in crop growth prediction, and future research directions span several key areas. These include multimodal data integration, which aims to enhance prediction accuracy by combining satellite imagery, meteorological data, and soil sensor information; climate change modeling to predict its impact on crop growth; the development of reinforcement learning and adaptive systems for real-time optimization of planting strategies; the advancement of precision agriculture to improve resource efficiency through more detailed predictions; the application of intelligent agricultural robots to reduce reliance on manual labor; and the enhancement of deep learning model training efficiency. These innovations will drive the advancement of agricultural technology, increase yields, protect the environment, and achieve sustainable agricultural growth.
