Abstract

Integrating convolutional neural networks (CNNs) with the Internet of Things (IoT) is paramount in agriculture, particularly greenhouses. By leveraging IoT capabilities, operators can collect agro-environmental information inside the greenhouse based on installed sensor nodes. This data-driven approach minimizes water, fertilizer, and energy waste. Simul-taneously, CNNs enhance the monitoring systems by facilitating early detection and classification of crop diseases. Our research proposes a comprehensive solution: an online technology platform for intelligent greenhouses based on IoT and CNNs. This platform effectively collects environmental and physical variables and detects diseases in real time using image-based analysis. The results of our study demonstrate that the system architecture is a reliable IoT platform, leading to significant energy savings. Moreover, the disease identification accuracy and classification process achieved an impressive rate of over 98 %, ensuring the system's efficacy in identifying and categorizing diseases. Additionally, the system exhibits a recall rate of over 90 %, indicating its ability to identify and recall crop disease instances accurately.