Automatic detection of agricultural plastic greenhouses using deep learning and aerial RGB images

Abstract. Rapid urbanization in developing countries such as Iran has intensified pressure on agricultural land, highlighting the need for sustainable and efficient food production systems. Agricultural Plastic Greenhouses (APGs) have become a scalable alternative by enabling year-round cultivation and optimized land utilization. However, their rapid expansion necessitates continuous monitoring to evaluate structural integrity and environmental impacts, including soil degradation, plastic waste accumulation, and water consumption. This study presents a deep learning-based framework for the automated detection and condition assessment of APGs using 0.5 m resolution Google Earth imagery across four major agricultural regions in Tehran County: Pakdasht, Qarchak, Pishva, and Varamin. The proposed pipeline integrates YOLOv11 for precise APG segmentation with a U-Net architecture employing a MobileNetV2 backbone for classifying damaged and intact structures. Out of 158,912 analyzed image tiles, 6,835 contained APGs, covering an estimated area of 18.73 km². Among these, 1,863 damaged structures were identified, predominantly located in Pakdasht and Pishva. Around 20% of the annotated greenhouses were verified on-site, improving labeling reliability, and the relatively standardized design of APGs in Iran suggests the model could generalize to similar regions, with minor fine-tuning using local samples if necessary. GIS-based spatial analysis further delineated potential plastic waste risk zones, supporting targeted environmental management. Comparison with government statistics and Sentinel-2 imagery from 2021 and 2024 revealed a continued shift toward greenhouse farming in response to declining cropland availability. The proposed framework provides a scalable and replicable tool for periodic APG monitoring, facilitating data-driven policymaking and sustainable agricultural planning.