Crop Yield Estimation Based on Time-Series Satellite Imagery and Artificial Intelligence Algorithms: A Review

The increasing global demand for agricultural production, limited water resources, climate change, and the need to ensure food security have made the development of accurate and intelligent approaches for monitoring and predicting crop yield a key issue in precision agriculture. In this study, we reviewed a crop yield estimation model based on integrating remote sensing data, climatic information, time-series satellite imagery, and artificial intelligence algorithms. Also, in some study, to derive information relevant to crop performance stated how used vegetation indices extracted from satellite imagery, together with climatic variables, are incorporated into the modeling framework. The capability of different artificial intelligence algorithms for crop yield modeling and prediction will be evaluated and compared to identify the most suitable approach. They illustrated the integration of satellite-derived information, vegetation indices, and climatic variables with intelligent modeling techniques could improve crop yield estimation accuracy compared with conventional methods and enable early yield prediction before harvest.

کلیدواژه ها:

Crop yield ، Satellite imagery ، Artificial intelligence algorithms ، Climatic variables