Soybean leaf disease detection and classification using deep learning approach
Year: 2025
Type: article
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.
Authors: Ayenew Kassie Adimas, Mareye Zeleke Mekonen, Tsehay Admassu Assegie, Hemant Kumar Singh, Indu Mazumdar, Shashi Kant Gupta, Ayodeji Olalekan Salau, Ting Tin Tin
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4413002128