Deep Learning–Based Crop Yield Prediction Using Multispectral Satellite Imagery

Abstract

Reliable estimation of crop yield is essential for effective agricultural management and ensuring food security. Conventional yield prediction approaches are often constrained by limited field data and their inability to represent spatial variability across large agricultural regions. With the increasing availability of multispectral satellite imagery, it has become possible to monitor crop growth and condition continuously throughout the growing season. However, transforming this large volume of remotely sensed data into accurate yield estimates remains a significant challenge. This research presents a deep learning–based approach for predicting crop yield using multispectral satellite imagery. Multiple spectral bands and vegetation-related indicators extracted from satellite data are used to characterize crop development patterns across time and space. A deep learning model is employed to automatically learn representative features from the multispectral inputs and establish a robust relationship between observed crop conditions and final yield values. The proposed framework is validated using multi-year satellite imagery and corresponding ground-based yield records. The results indicate that the proposed deep learning approach achieves higher prediction accuracy compared to conventional machine learning techniques. In addition, the model demonstrates strong potential for early-season yield estimation, enabling timely decision-making for farmers, agricultural planners, and policymakers. The study highlights the effectiveness of deep learning and remote sensing integration as a scalable solution for crop yield prediction across diverse agricultural environments.