AI System Cuts Agricultural Subsidy Fraud Detection Work in Half
Researchers have developed a machine learning tool that automatically detects unauthorized structures and land changes on agricultural parcels with 91% accuracy, potentially cutting government inspection costs by half. The finding could accelerate subsidy compliance checks across Europe and enable cash-strapped agricultural authorities to redirect resources to higher-value oversight.
Originaltitel: Object Identification in Land Parcels Using a Machine Learning Approach
<p>This paper introduces an AI-based approach to detect human-made objects and changes in these on land parcels. To this end, we used binary image classification performed by a convolutional neural network. Binary classification requires the selection of a decision boundary, and we provided a deterministic method for this selection. Furthermore, we varied different parameters to improve the performance of our approach, leading to a true positive rate of 91.3% and a true negative rate of 63.0%. A specific application of our work supports the administration of agricultural land parcels eligible for subsidiaries. As a result of our findings, authorities could reduce the effort involved in the detection of human made changes by approximately 50%.</p>