Abstract
The increasing generation of waste and depletion of natural resources has led to a growing need for innovative approaches for utilizing different types of waste as potential energy and material resources. Agricultural activities produce large amounts of Agricultural Waste (AW), which, if not adequately managed, can lead to environmental degradation. One potential solution for the effective utilization of AW is converting it into biogas. However, commercializing this process requires a comprehensive understanding of the types and quantities of AW generated. In this paper, the use of a Fully Convolutional Neural Network (FCN), which has rapidly advanced with the progress of Artificial Intelligence and become essential for tasks such as Semantic Segmentation, Object Detection, and Image Classification, is proposed to improve the prediction of AW for biogas production. Furthermore, this paper presents a Deep Learning-based image segmentation method to recognize vineyard fields, which are a significant source of AW, using remote satellite images. The proposed approach can significantly improve the identification of AW sources, and thus contribute to the efficient and sustainable utilization of AW for biogas production.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Facta Universitatis, Series: Working and Living Environmental Protection
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
