Authors - Prawit Chumchu, Kailas Patil, Alfa Nyandoro Abstract - Automated cannabis cultivation faces considerable challenges, primarily due to complex environmental control requirements and timely disease detection. To address these issues, this study develops and evaluates a novel cannabis growing system integrated with Artificial Intelligence of Things (AIoT), aimed at automating environmental parameters and disease management. The proposed AIoT-based system autonomously regulates humidity, temperature, lighting, nutrients, water, and medicinal treatments to optimize cannabis plant health. Central to this automation is an intelligent disease detection module leveraging deep learning techniques capable of classifying cannabis leaf conditions into five categories: Healthy, Malnutrition, Red Spider Mites, Bacterial Spot, and High Temperature Stress. Initially, we assessed five pretrained convolutional neural networks (CNNs): InceptionV3, Xception, ResNet50, ResNet50V2, and ResNet152V2, which achieved accuracies up to 100% after 100 epochs. Subsequently, we developed simplified CNN models specifically optimized for deployment on low-cost edge devices, such as the Raspberry Pi, achieving the same high accuracy (100%) while significantly reducing computational complexity. These optimized models were integrated into our AIoT system, successfully automating real-time adjustments of critical growing conditions. Our findings underscore the potential of AIoT technologies in transforming cannabis agriculture by providing accurate, efficient, and scalable solutions for disease detection and cultivation management, thus enabling broader adoption of intelligent, automated agricultural practices.
