Abstract Greenhouse farming plays a vital role in enhancing agricultural productivity, yet it often suffers from inefficient resource management and delayed disease detection. This paper presents a novel solar-powered Smart Greenhouse Management System (SGHMS) that integrates IoT-based environmental monitoring, machine learning for real-time disease detection, and a Raspberry Pi-controlled autonomous sprayer into a unified platform. Unlike existing systems, our approach combines a CNN-based plant health classifier deployed locally on Raspberry Pi with an energy-efficient solar power source to ensure reliable off-grid operation. A user-friendly web and mobile application enables real-time monitoring, alert generation, and remote control of environmental parameters and spraying actions. The system was deployed in a real greenhouse for 30 days and demonstrated a 92% disease detection accuracy while significantly reducing water and energy consumption. This integrated solution offers a scalable and cost-effective approach to sustainable precision agriculture, particularly in resource-constrained regions.
Hussain et al. (Tue,) studied this question.
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