International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | ReHarvest.AI: A Multimodal Deep Learning and Smart Routing Framework for Sustainable Fruit Waste Reduction |
|---|---|
| ABSTRACT | Fruit waste, particularly in the fruit supply chain, poses a significant challenge to sustainability, contributing to economic losses and environmental degradation. Inefficient identification of fruit spoilage stages and lack of timely redistribution mechanisms often result in large quantities of edible fruit being discarded. This paper presents ReHarvest.AI, an intelligent fruit waste management and redistribution system designed to minimize waste through real-time decision-making and deep learning techniques.The proposed system utilizes a Convolutional Neural Network (CNN)-based model to classify fruits into multiple spoilage stages, including fresh, near spoilage, and spoiled, enabling accurate assessment of fruit quality. In addition, the system incorporates a predictive module to estimate remaining shelf life using environmental and temporal data. Based on these insights, a smart routing engine dynamically directs consumable fruits to non-governmental organizations (NGOs) for donation, while spoiled fruits are redirected to composting or biogas production units.ReHarvest.AI further integrates real-time inventory monitoring, logistics optimization, and stakeholder coordination through a cloud-based architecture. The platform provides role-based dashboards, live tracking of pickups, and automated alerts to ensure timely action and reduce delays in redistribution. An analytics module evaluates key performance indicators such as waste reduction, donation efficiency, and environmental impact, including CO₂ emission savings.The system is implemented using a scalable architecture with a React-based frontend, Spring Boot backend, and PostgreSQL database, ensuring efficient performance across web platforms. Experimental results demonstrate improved accuracy in spoilage detection and a significant reduction in fruit waste through optimized redistribution strategies.The proposed solution contributes to sustainable supply chain management by combining artificial intelligence, real-time analytics, and collaborative logistics, ultimately supporting communities, reducing environmental impact, and promoting responsible resource utilization. |
| AUTHOR | MOHAMMED YOUSUF, PAVITRA LAXMAN KHANGAONKAR, LEKHANA D, LAKSHMI R, PROF. ARCHANA K N UG Students, Dept. of CSE, Jain Institute of Technology, Davangere, Karnataka, India Assistant Professor, Dept. of CSE, Jain Institute of Technology, Davangere, Karnataka, India |
| VOLUME | 183 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1404055 |
| pdf/55_ReHarvest.AI A Multimodal Deep Learning and Smart Routing Framework for Sustainable Fruit Waste Reduction.pdf | |
| KEYWORDS | |
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