AI-Integration for Enhancing Value Addition in Agri-Processing Industries
DOI:
https://doi.org/10.5281/zenodo.21566368Keywords:
Human-AI Interaction, Convolutional Neural Network (CNN), IOT sensors, Edge-cloud hybrid systems, SpectroscopyAbstract
The agricultural sector and food processing are now undergoing a revolutionary change from industry 4.0 to industry 5.0, by correlation of Human-AI Interaction, Decentralized control, and enhanced sustainability in agricultural processing industry. This study shows the integration of artificial intelligence across the entire food value chain. AI applications use machine learning models, sensor networks, and drive systematic resilience. The key applications include pest and disease management using a Convolutional Neural Network (CNN), remote sensing data study, and early detection. There are three predominant agricultural prototypes that enable positioning: distributed multi-agent systems, monolithic autonomous machines, and edge-cloud hybrid systems that balance computational and evaluate genuinely with high precession. Advanced quality assessment employee spectroscopy, electronic sensor systems, and computer vision are used. Sustainability initiatives were supported by Minimizing Food Waste, Optimizing Stoke Rotation, predicting shelf life, and Intelligent Packaging solutions through integrating IOT sensors and AI algorithms. Supply chain optimization influences anticipative analytics for logistic planning, block chain-enabled traceability, real-time route adjustments, and cold-chain resilience. Conjointly, these Human-AI Interaction drive innovations to improve Product quality, traceability, and sustainability in the agricultural processing sector, reducing resource consumption and environmental consequences.
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Copyright (c) 2026 Vadlapudi Dinesh Kumar, D. Pradeep Kumar
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