End-to-End Network for Early Crop Planting Area Prediction

Early prediction of crop planting areas is essential for agricultural planning and management. Traditional two-stage calculation methods face limitations due to remote sensing imagery classification and are prone to significant area predicting errors due to ‘error accumulation.’ This paper introduces an innovative end-to-end network designed for accurate, large-scale early-stage crop area prediction. Leveraging multi-subimages processing and ‘trusted pixel’ technology, our approach overcomes the technical challenges of conventional methods, by extracting features from multi-year land cover data, our model directly predicts crop planting areas. Experiments on estimating corn and soybean areas in the USA show a relative error below 4%. Our research lays a foundation for early-stage crop monitoring, supports sustainable agricultural practices, and provides actionable insights for agricultural decision-making and resource allocation.