Aug 2026· Legume Research An International Journal· 0 citations· 38 references
TL;DR
The results support the model’s potential for integration into smart agricultural systems for early disease detection in cowpea cultivation and support the model’s potential for integration into smart agricultural systems for early disease detection.
Abstract
Background: Cowpea is a drought-resilient legume that is important in India, Africa and parts of Asia. In India, it is widely cultivated in arid and semi-arid regions, supporting rural nutrition and income. Leaf diseases, however, significantly reduce yields, making AI-based detection systems essential for timely and accurate diagnosis. Methods: This study presents a deep learning framework using DenseNet121 for multi-class classification of cowpea leaf conditions: Bacterial wilt, septoria leaf spot, mosaic virus and fresh leaves. A total of 2,273 annotated images were curated, preprocessed and divided into training, validation and test sets. Real-time data augmentation and transfer learning techniques were employed to improve model generalization. The model was trained using categorical cross-entropy loss and evaluated with various metrics. Result: The model achieved a test accuracy of 93.87%, with strong F1-scores and precision across all four classes. The matthews correlation coefficient (MCC) was 0.9184, indicating high reliability, while the multi-class ROC-AUC score reached 0.9931, showing excellent class separability. Confusion matrix and precision–recall analysis further confirmed robust performance, especially for bacterial wilt and Septoria leaf spot. These results support the model’s potential for integration into smart agricultural systems for early disease detection in cowpea cultivation.
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
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: Legumes, such as beans, are important in worldwide agriculture because of their nutritional value and soil-enriching qualities. However, bean crops are susceptible to diseases such as angular leaf spot and rust, which may reduce production and quality. Disease identification that is both effective and timely is essential to crop health and output. Traditional diagnostic approaches are often labor-intensive and susceptible to inaccuracy. Recent advances in deep learning (DL) provide interesting possibilities for automating disease categorization, possibly improving accuracy and efficiency. Methods: This study evaluates and compares the performance of two deep learning architectures, ResNet50 and VGG19, for the classification of bean leaf diseases. The dataset, sourced from Kaggle, comprises 1295 images categorized into three classes: Angular Leaf Spot, Rust and Healthy. Both systems relied on pre-trained ImageNet weights, with adjustments customized to the classification objective. The models were trained for 25 epochs and their performance was assessed based on overall accuracy. Result: The performance of the models is evaluated in terms of the confusion matrix, classification report and ROC(AUC) curves. The ResNet50 model achieved an overall accuracy of 93.75%, while the VGG19 model attained an accuracy of 91.41%. The findings indicate that ResNet50 performs better than VGG19 in terms of classification accuracy. This work demonstrates ResNet50’s performance for bean leaf disease classification tasks, providing important insights for future research and practical applications in agricultural disease control.
Yu-Yan Xu, Hui-Qing Chen, Qing-Mei Lin· Legume Research An Internati...· 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
Paddy (Oryza sativa L.) is a strategic staple food commodity in Indonesia, yet its production is frequently disrupted by various plant diseases that cause significant yield losses each year. Conventional visual disease identification is inefficient and prone to error, necessitating the adoption of more reliable, automated diagnostic technology. This study compares the performance of two pre-trained convolutional neural network architectures, VGG16 and ResNet50, in classifying rice leaf images into four categories: Brown Spot, Leaf Blast, Healthy Rice Leaf, and Rice Hispa. Both models were fine-tuned using transfer learning under an identical experimental configuration, including the same data split, optimizer, learning-rate schedule, and augmentation pipeline, to ensure a controlled architectural comparison. Model evaluation was conducted using accuracy, precision, recall, F1-score, the Matthews Correlation Coefficient (MCC), confusion matrix analysis, training convergence behaviour, inference-time computational efficiency, and Grad-CAM interpretability visualization. Experimental results show that ResNet50 achieved a test accuracy of 99% and an MCC of 0.9874, outperforming VGG16, which achieved a test accuracy of 95% and an MCC of 0.9306. Confusion matrix analysis revealed that ResNet50's errors were concentrated almost exclusively within the visually similar brown spot–leaf blast class pair, whereas VGG16 exhibited additional confusion between the healthy rice leaf and rice hispa classes. ResNet50 also demonstrated faster and more stable training convergence, more spatially coherent Grad-CAM activation patterns, and substantially higher throughput under batched inference (356.27 vs. 207.63 images/second at batch size 32), while VGG16 retained a marginal latency advantage under single-image inference. These findings indicate that, under the configuration examined in this study, ResNet50 is the more suitable architecture for rice leaf disease classification, offering an advantageous combination of accuracy, interpretability, and computational efficiency for potential deployment in agricultural monitoring systems.
Valda Laura Uswary, Amriana Amriana· JOURNAL OF APPLIED INFORMATI...· 0 citations
Early and accurate detection of plant diseases is critical in precision agriculture to improve crop management and yield. Mungbean (
Vigna radiata
L.) is highly susceptible to several foliar diseases, including yellow mosaic, powdery mildew, leaf crinkle, and cercospora leaf spot, which cause substantial productivity losses. Despite expanding applications of deep learning in plant disease diagnosis, systematic multi-architecture evaluation for mungbean disease classification under natural field conditions remains limited. This study addresses this gap by evaluating five state-of-the-art deep convolutional neural network (DCNN) architectures on a large-scale, field-acquired mungbean dataset that captures real-world variability across environmental conditions and disease severity levels, distinguishing it from controlled laboratory studies. A total of 5,617 original images across five classes were used. Data augmentation was applied exclusively to the training subset after stratified splitting to prevent data leakage. The dataset was partitioned into training (70%), validation (15%), and testing (15%) subsets. VGG16, VGG19, ResNet50V2, DenseNet121, and InceptionV3 were evaluated using identical transfer learning and fine-tuning protocols. Model performance was assessed using AUC-ROC, Cohen's kappa coefficient, McNemar's test for pairwise statistical comparisons, five-fold cross-validation, and Grad-CAM-based interpretability. On the independent test set, InceptionV3 achieved the highest accuracy (98.47%) and macro-F1 (98.49%), followed by VGG16 (98.36%) and VGG19 (97.89%). AUC-ROC values exceeded 0.997 for all models, confirming excellent class discrimination. Grad-CAM visualizations further confirmed that model predictions were based on biologically relevant disease symptoms. The findings demonstrate the effectiveness of deep learning for robust disease recognition under realistic field conditions and highlight the potential of AI-based diagnostic tools for crop health monitoring, precision agriculture, and decision-support systems in mungbean production.
Shail Bala, S. I. Harlapur, A. Kanade et al.· Frontiers in Artificial Inte...· 0 citations
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