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Modern agriculture faces critical challenges such as climate change, food security and supply chain inefficiencies, which demand innovative solutions. Traditional farming systems often lack real time monitoring, data security and transparency, leading to wastefulness and quality concerns. To address these, we present a comprehensive precision agriculture framework that integrates Internet of Things (IoT) sensors, Raspberry Pi (R-Pi) edge computing, blockchain based data management and computer vision (CV) assisted statistical modeling. The system collects environmental data via a sensor network, processes it at the edge using R-Pi, and records summarized outputs on a secure Ethereum based blockchain using smart contracts. Simultaneously, CV modules perform real time quality assessment and anomaly detection. A Markov chain based stochastic model is employed to track quality degradation in high value crops. The methodology is validated through a saffron use case, demonstrating effectiveness in monitoring filament degradation and detecting potential fraud. This integration enhances real time decision making, ensures traceability and promotes sustainability in climate smart agriculture.
Safeer et al. (Wed,) studied this question.