Pioneering automation in agricultural subsidy processing through deep learning for computer vision

Validating farmers’ eligibility for subsidies under the European Union’s Common Agricultural Policy (CAP) requires substantial resources. An automated solution can minimize human errors and save time. This study leverages deep learning to automate the validation process using ground-level landscape images (GLLP), an approach not previously explored. We conducted two rigorous experiments to identify the optimal model architecture and assess the impact of data cleaning, accurate labelling, reduced feature ambiguity, and stricter photo collection protocols on performance. Our results reveal that while the ResNet-18 architecture achieved a recall of 0.61 on the original dataset, distinguishing between eligible and ineligible grasslands was challenging. By addressing image quality and enforcing standardized data collection, accuracy improved to 0.89 and recall to 0.94. Crucially, our research emphasizes the development and validation of effective data collection strategies, ensuring uniformity and high-quality data essential for accurate model performance. Our findings affirm the potential of deep learning to automate subsidy eligibility classification, contingent on resolving challenges related to the quality and standardization of GLLP data.