Classification of Angular Leaf Spot, Bean Rust Diseases in Haricot Bean Leaves using CNN and Transfer Learning
Haricot beans are a highly valuable legume that is increasingly in demand on the market. It is an important crop for Ethiopia in terms of earning foreign currency. The classification of haricot bean leaf diseases is done manually in Ethiopia by experts. It requires a lot of effort, takes a lot of time, and has accuracy and consistency issues. The purpose of this study is to minimize the effort to classify the disease accurately using deep learning and image processing. The required images of haricot beans were taken in Wentu Bereha, and Kork in the Ethiopian Amhara region’s Baso Liben Woreda. 2400 sample images of healthy haricot beans, angular leaf spots, and bean rust were collected. A smartphone camera with a 4080x3060 pixel resolution was used to get images. In addition, various classifiers like CNN, VGG, MobileNet, ResNet50, and GoogleNet were used for classifying according to their predefined classes. In this study, the Python programming language was employed to conduct the experiments. In this study, six groups of experiments applying various algorithms (CNN) and transfer learning methods (VGG16, VGG19, ResNet50, ResNet50V2, GoogleNet/InceptionV3& MobileNetV2) were carried out. The experiment’s findings showed that the CNN classifier from scratch had a testing accuracy of 87.5%, training accuracy of 88.04%, and validation accuracy of 89.57%. Furthermore, testing accuracy for the VGG16, VGG19, ResNet50, ResNet50V2, InceptionV3, and MobileNetV2 classifiers that use transfer learning techniques was shown to be 90.2%, 91.08%, 92.86%, 58.1%, 47%, and 99.9%, respectively. The model’s accuracy increased or decreased when transfer learning classifiers were used. It was determined that the model can classify the diseases of haricot bean leaf with the best percentage of accuracy in some applied techniques especially in MobileNetV2. It is advised that the developed strategy be applied to additional varieties of haricot beans, such as white beans and speckled beans.
Authors
- Kebad Nigussie Werku
- Ibrahim Gashaw Kassa
- Abinet Bizuayehu Desta
- Dessalegn Melkie Ayal
- Mesenbet Abewa Bimrew
- Misganaw Ketema Ayele
Institutions
- Debark University (ET)
- University of Gondar (ET)
Publication Details
- Published
- 2024-11-18
- DOI
- https://doi.org/10.1109/ict4da62874.2024.10777237
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00