Semantic segmentation for mapping agricultural waste sources: A vineyard case study for energy valorization via biogas production

Given the growing trend of increasing waste and diminishing resources, considerable efforts are being directed toward developing innovative methods for utilizing various types of waste as potential energy and material resources. Agriculture generates large quantities of waste, and inadequate management of this waste can cause severe environmental challenges. Transforming agricultural waste (AW) into biogas presents an excellent opportunity for its effective use; however, commercializing this process requires a comprehensive understanding of potential AW sources, primarily the types and quantities of waste generated. Consequently, this paper proposes a deep learning-based image segmentation approach for identifying potential AW sources using remote sensing images. The research examines the effectiveness of the DeepLabV3+ with various backbone networks for semantic segmentation with an emphasis on detecting vineyards as potential contributors to agricultural waste for biogas generation.