Crop modeling has evolved significantly over the past few decades, driven by advances in technology and a growing need for precision in agriculture. As the agricultural sector faces increasing pressures from climate change, resource constraints, and the demand for higher yields, integrating Artificial Intelligence (AI) and real-time data into crop modeling is emerging as a transformative solution. This integration promises to enhance the accuracy of crop predictions, optimize resource use, and improve overall crop health monitoring. This article explores the future of crop modeling with a focus on how AI and real-time data are shaping this field and the implications for effective crop health monitoring.
The Future of Crop Modeling Integrating AI and RealTime Data