Machine Learning-based Automated Detection and Multi-class Classification of Faba Bean Leaf Diseases using a VGG16 based Deep Convolutional Neural Network
Aug 2026· Legume Research An International Journal· 0 citations· 27 references
TL;DR
A machine learning-based automated framework for multi-class classification of Faba bean leaf diseases using transfer learning with the VGG16 convolutional neural network demonstrates the effectiveness of transfer learning for plant disease detection and highlights its potential for scalable, automated crop health monitoring in precision agriculture.
Abstract
Background: Faba bean is an important legume crop valued for its nutritional and soil-enriching benefits, yet its productivity is severely affected by foliar diseases. Automated image-based detection using deep learning provides a rapid and reliable approach for early disease identification and improved crop management. Methods: This study developed a machine learning-based automated framework for multi-class classification of Faba bean leaf diseases using transfer learning with the VGG16 convolutional neural network. A dataset of 8,021 RGB images collected under natural field conditions was used, comprising four classes: healthy, rust, gall and chocolate spot. Images were resized to 224 × 224 pixels and normalized prior to training. The pretrained convolutional layers of VGG16 were frozen and a custom classification head with global average pooling and dropout regularization was added. Model performance was evaluated using classification metrics. Result: The proposed model achieved an overall classification accuracy of 92.34% and a macro-averaged F1-score of 0.9227 on the test dataset. Strong classification performance was observed across all disease categories, with particularly high predictive accuracy for healthy and rust classes. The findings demonstrate the effectiveness of transfer learning for plant disease detection and highlight its potential for scalable, automated crop health monitoring in precision agriculture.
Findings indicate that the lightweight MobileNet architecture can effectively support reliable and scalable faba bean disease detection under real-world agricultural conditions.
Kil-hwan Shin· Legume Research An Internati...· 0 citations
Background: Groundnut is a vital crop affected by several foliar diseases, such as leaf spot, alternaria, rust and rosette. These diseases can reduce crop quality and yield. Manual identification is time-consuming and may lack accuracy. Deep learning methods offer a reliable alternative for automated disease detection. Methods: This study proposes a fine-tuned InceptionV3 convolutional neural network to classify five groundnut leaf classes. A dataset (Sourced from the Mendeley database) of 1,720 high-resolution images was used. These were collected under natural conditions from fields in Ramchandrapur village, West Bengal, India. Images were resized, normalized and augmented to improve model generalization. Transfer learning was applied using the InceptionV3 base model. A custom classification head was added with dense layers, batch normalization, dropout and L2 regularization. The model was trained with the RMSprop optimizer and evaluated using performance matrices, area under the curve (AUC) and Cohen’s Kappa. Result: The proposed model achieved a test accuracy of 98.26%. The macro average F1-score was 0.9872 and cohen’s kappa reached 0.9762. AUC values were above 0.998 for all classes. The model showed excellent performance, especially for minority classes like rosette and rust. It correctly classified almost all samples, with very few misclassifications. Compared to earlier studies, the model performed competitively and offered high interpretability and efficiency. These results support its use in real-world disease diagnosis in agriculture.
Zhe Li, Xue-Lu Qiu· Legume Research An Internati...· 0 citations
This study improves potato leaf disease detection using a fine-tuned InceptionV3 with data augmentation and dropout, while Grad-CAM visualizations enhance model interpretability, reliability, and practical utility for accurate agricultural disease diagnosis.
Aradhy Tiwari, Amit Saxena, Chandrashekhar Chandrashekhar· Indian Journal of Science an...· 0 citations
Diseases that affect plants considerably lower the production of crops and pose a significant threat to global food security, particularly in high-value crops like bell pepper (Capsicum annuum L.). The timely and sensitive identification of the disease is essential in reducing losses related to yield and paving the way to sustainable management of crops. Although automated plant disease diagnosis has been improved by artificial intelligence (AI)-driven image analysis, single-model methods are not always robust or generalisable. The study proposes a hybrid convolutional neural network-random forest (CNN-RF) framework for the early detection of bell pepper leaf diseases. Deep visual features of leaf images were extracted and subsequently used with a pretrained ResNet50 model and then used to classify the images with the help of a RF ensemble algorithm. Experiments were conducted on a publicly available PlantVillage bell pepper dataset comprising 495 leaf images (199 bacterial spot and 296 healthy samples). The classification accuracy of the proposed hybrid model was 97.98 % with high class-wise precision, recall and F1-scores. Receiver operating characteristic (ROC) analysis produced an area under the curve (AUC) of 0.98, indicating excellent discriminative performance. The dataset was evaluated using a stratified 80 : 20 train-test split. The results show that deep feature extraction combined with an ensemble learning method can improve classification accuracy and offer a scalable solution to assist in precisionagriculture and timely disease control. However, the framework was evaluated only on the PlantVillage dataset and has not yet been validated under real-field agricultural conditions. The framework demonstrates potential for extension to multi-disease classification tasks, although further validation on field-acquired datasets is required.
J. G. Grace, M. Shehu· Plant Science Today· 0 citations
The findings indicate that the combination of ResNet50 and Grad-CAM is effective for plant disease classification and provides better explainability for deep learning-based agricultural applications.
W. Zalmi, Rahmi Putri Kurnia, Dyah Listianing Tyas· Informatik : Jurnal Ilmu Kom...· 0 citations
A deep learning-based solution to automate disease detection of groundnut leaf conditions that outperformed existing methods such as ResNet50, CNN with progressive resizing, LeafNet and LeafNet and maintained low training and validation loss throughout training.
Jie-Shin Lin, Y. Tai, Suh-Chen Hsiao et al.· Legume Research An Internati...· 0 citations
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