SOYBEAN LEAF DISEASE CLASSIFICATION USING MOBILENETV2 WITH LIGHTWEIGHT DEEP LEARNING IN RESOURCE CONSTRAINED AGRICULTURAL ENVIRONMENT

Abstract

Soybean is one of the most significant oilseed crops in the world and is highly susceptible to a number of diseases of the leaves that cause losses in quality and yield. Early and accurate detection of disease is vital for successful early disease management and sustainable agriculture production. In this study, a lightweight deep learning framework MobileNetV2 is proposed for an automatic soybean leaf disease diagnosis system in resource-poor agricultural settings. Images of 2000 soybean leaves were downloaded from agricultural fields of the Pakistan region and a region-specific dataset named SDD-2025 was developed. The data is split into three disease classes: Charcoal Rot, Mosaic Virus and Pest Infestation. Pre-processing methods such as rotation, flipping, zooming and brightness adjustment were used to augment the data to help prevent overfitting and to make the model more generalizable. The feature extraction and classification was performed using transfer learning with a pretrained MobileNetV2 architecture. The dataset was split in the ratio 70:15:15 for training, validation and testing respectively. The experimental results were evaluated by calculating accuracy, precision, recall, F1 score and ROC analysis. The proposed model successfully classified the soybean diseases with 96.14% accuracy, which shows the effectiveness of lightweight CNNs to recognize soybean diseases. The trained framework yields an automated and efficient early disease diagnosis solution, which requires minimal human involvement. Moreover, the low weight of the model makes it well suited for implementing real-time agricultural monitoring and smart farming applications. Although the study results are positive, the number of records and geographical diversity are limited. In order to make the framework more robust and generic, it will be crucial to expand the data set, add more illness classes, and test it under various climatic conditions