Unsupervised Deep Learning for Plant Disease and Pest Identification: A Comprehensive Approach

The agriculture industry plays a crucial role in global food security, but plant diseases and pests pose significant challenges. Our paper presents an innovative approach using unsupervised deep learning for automated plant disease and pest detection. Unlike traditional manual methods, our approach leverages deep and variational autoencoders to analyse plant photos and sensor data, achieving high precision across different crops and environmental conditions. This research advances precision agriculture, enhances sustainability, and boosts productivity. It pioneers a custom CNN and CAE network for plant disease diagnosis, which is not currently achievable with existing technology. We applied this approach to identify Bacterial Spot and other plant diseases using diverse plant images from an online photo-sharing service.