AgriCycle AI: An AI-Powered Smart Waste-to-Wealth Platform for Agricultural Waste Identification, Valorization and Marketplace Connectivity

Abstract

Agricultural production generates large quantities of crop residues, husks, stalks, fruit and vegetable waste, bagasse, manure and other biological by-products, which are frequently burned, dumped or left unmanaged. This paper presents AgriCycle AI, a proposed software-only, intelligent waste-to-value platform that combines computer vision, machine learning, recommendation logic, economic estimation, environmental-impact estimation and digital marketplace functionality to convert agricultural waste from a disposal problem into an economic and environmental opportunity. A farmer uploads an image of agricultural waste, which is validated, preprocessed and classified by an AI model that returns a probable waste category with a confidence score; a recommendation engine then identifies suitable utilization pathways, an indicative value is estimated, and a marketplace module connects the material with potential buyers or processors. The architecture separates the frontend, API/backend, AI inference, recommendation, marketplace, database and analytics layers so each module can be developed and validated independently. This paper reviews related work in agricultural-waste management, computer vision and digital marketplaces, and describes the proposed architecture, methodology, database design and evaluation plan for the system as an academic prototype requiring further dataset collection, model training and field validation before production deployment.