An AI-Integrated Renewable-Powered Cold Storage System with Advanced Environmental Sensing for Smallholder Farmers
Received: 10 August 2025 | Revised: 7 September 2025 and 21 September 2025 | Accepted: 24 September 2025 | Online: 15 October 2025
Corresponding author: Sahana D. Gowda
Abstract
Smallholder farmers often face significant post-harvest losses due to the lack of affordable and efficient cold storage infrastructure, which limits their ability to maintain production quality and capitalize on favorable market conditions. Conventional storage systems are energy-intensive, rely on grid power, and lack real-time monitoring or predictive spoilage management. To address these challenges, this study presents an AI-integrated renewable-powered cold storage system equipped with advanced environmental sensing and an edge-based Long Short-Term Memory (LSTM) model for spoilage prediction and freshness assessment. The proposed method combines solar Photovoltaic (PV) energy, Phase Change Material (PCM)-assisted hybrid cooling, multi-sensor environmental monitoring (temperature, humidity, CO2, ethylene, and NIR spectral sensing), and edge AI-powered freshness indexing to enable proactive quality control and informed market dispatch decisions. Experimental validation demonstrated a 28% reduction in grid energy use, shelf life extension of up to 4×, high spoilage prediction accuracy (RMSE: 0.47 days, MAE: 0.31 days), and a 22% increase in farmer income with a 2.1× RoI over three years. These findings establish the proposed system as a scalable, energy-efficient, and intelligent solution for reducing post-harvest losses and enhancing socio-economic outcomes for smallholder farmers. Future work will focus on scalable AI model enhancements, blockchain-based traceability, and dynamic pricing intelligence to further strengthen system performance and impact.
Keywords:
renewable-powered cold storage, edge AI, spoilage prediction, shelf life extension, smallholder farmers, LSTMDownloads
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