Plant Disease Classification of Basal Bulb Rot in Shallots Using Vision Transformer
K. R. Dehaleesan *
Department of Information Technology, Dhanalakshmi Srinivasan Engineering College (Autonomous), Perambalur, Tamil Nadu, India.
T. Balaji
Department of Information Technology, Dhanalakshmi Srinivasan Engineering College (Autonomous), Perambalur, Tamil Nadu, India.
S. Ramakrishnan
Department of Information Technology, Dhanalakshmi Srinivasan Engineering College (Autonomous), Perambalur, Tamil Nadu, India.
M. Venkatachalapathy
Department of Information Technology, Dhanalakshmi Srinivasan Engineering College (Autonomous), Perambalur, Tamil Nadu, India.
V. Thiruppathy Kesavan
Department of Information Technology, Dhanalakshmi Srinivasan Engineering College (Autonomous), Perambalur, Tamil Nadu, India.
*Author to whom correspondence should be addressed.
Abstract
Early detection of plant diseases is critical for sustainable agriculture and reducing crop losses. This study presents a real-time monitoring system integrating IoT and machine learning for the early detection of basal bulb rot disease in shallots. The system combines image data captured by an ESP32-CAM and soil pH data from a sensor to provide timely alerts to farmers. The images undergo preprocessing using Gaussian filtering and histogram equalization, while pH data is smoothed using a moving average filter. Features such as color, texture, shape, and pH dynamics are extracted and analyzed using a hybrid classification model comprising MobileNetV2 for image-based disease identification and Random Forest for soil pH classification, fused at the decision level. The models were optimized for edge deployment using TensorFlow Lite and field-tested under solar-powered conditions. Experimental results demonstrate a hybrid model accuracy of 93.7%, with recall and specificity of 94.1% and 92.8%, respectively. The system responds within 450 milliseconds, making it suitable for real-time applications. This solution offers a low-cost, scalable, and accurate method for precision agriculture, reducing dependence on manual inspections and enabling proactive disease management.
Keywords: Bulb rot, plant disease, machine learning, monitoring