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All Journal Bulletin of Electrical Engineering and Informatics
Gupta, Shashi Kant
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Electrical & Electronics Engineering

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Soybean leaf disease detection and classification using deep learning approach Adimas, Ayenew Kassie; Mekonen, Mareye Zeleke; Assegie, Tsehay Admassu; Singh, Hemant Kumar; Mazumdar, Indu; Gupta, Shashi Kant; Salau, Ayodeji Olalekan; Tin, Ting Tin
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.8585

Abstract

In Ethiopia, where soybeans are mainly involved, manual observation has traditionally been relied upon for detecting soybean leaf diseases. However, the manual process is susceptible to numerous issues such as labor-intensiveness, inconsistency, and subjectivity. While previous studies have explored automated classification for soybean leaf disease detection, they primarily focused on binary classification, overlooking the complexity and diversity of soybean leaf diseases, which hinders effective management strategies. This study introduces deep learning algorithms and computer vision for automated soybean leaf disease identification and classification in soybean leaves. By comparing pre-trained convolutional neural network (CNN) models (VGG16, VGG19, and ResNet50V2), a dataset of 3078 soybean leaf images was curated, representing various diseases. Image preprocessing techniques augmented the dataset to 6,958 images, enhancing the model's accuracy and generalization performance. VGG16 demonstrated outstanding performance with a test accuracy of 99.35%, highlighting its promising performance and generalization potential.
Co-Authors Adimas, Ayenew Kassie Assegie, Tsehay Admassu Mazumdar, Indu Mekonen, Mareye Zeleke Salau, Ayodeji Olalekan Singh, Hemant Kumar Tin, Ting Tin