Aug 2026· Legume Research An International Journal· 0 citations· 33 references
TL;DR
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.
Abstract
Background: Groundnut farming is affected by several leaf diseases that reduce crop yield. Farmers often rely on visual inspection, which can be inaccurate and time-consuming. Early and precise identification of leaf diseases is essential for effective crop management. This study presents a deep learning-based solution to automate disease detection. Methods: A modified EfficientNetB0 architecture is proposed for classifying five types of groundnut leaf conditions: healthy, leaf spot (early and late), alternaria leaf spot, rust and rosette. The dataset is sourced from the Mendeley database that was collected from Ramchandrapur village in West Bengal, India, under natural lighting. A total of 1,720 images were captured using a DSLR camera. After verification and cleaning, the dataset was split into 1,204 training and 516 testing images. All images were resized, normalized and label-encoded. Data augmentation techniques such as rotation, flipping and zoom were used to improve generalization. Regularization was applied to reduce overfitting. The model was trained for 100 epochs using the RMSprop optimizer and early stopping. Result: The model achieved a test accuracy of 99.22%. Evaluation metrics confirm high performance across all classes. The model outperformed existing methods such as ResNet50 (82.3%), CNN with progressive resizing (96.12%) and LeafNet (97.23%). It also maintained low training and validation loss throughout training. These results highlight the model’s robustness, accuracy and potential for real-time field applications. The approach is lightweight and suitable for mobile-based disease detection tools.
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
The state-of-the-art deep learning methods for detection and classification are applied on banana leaf dataset and healthy and two common diseases of banana leaves are classified in this work.
N. Vidhya, R. Priya· AI Computer Science and Robo...· 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 modified AlexNet architecture for classifying field-captured tomato leaf images into seven disease categories was developed and generally focused on symptom-bearing leaf regions, whereas target spot was the most difficult category to classify.
Debabrat Bharali, Kanak C. Bora, Rashel Sarkar et al.· Journal of Scientific Resear...· 0 citations
Background: Accurate and timely diagnosis of foliar diseases is the most crucial factor in efforts to maximize crop yield and ensure sustainability. Existing deep learning models, especially single-backbone CNNs, have achieved promising results; however, they often fail to generalize well in different orchard conditions. Methods: In this study, the AFS-PLDCNet framework has been proposed for robust leaf disease classification. This framework uses an attention-based feature-fusion approach to combine deep representations extracted from EfficientNetV2S, MobileNetV2 and ResNet18. A learnable attention mechanism assigns adaptive weights dynamically to each feature and a lightweight meta-learner is used for classification. A new dataset of 9000 apple leaf images was captured in Himachal Pradesh’s orchards, encompassing Alternaria leaf blotch, Marssonina blotch and healthy leaves. Result: The experimental results demonstrate that AFS-PLDCNet achieved superior classification accuracy compared to existing single-backbone CNNs. The proposed model is well-suited for real-time, field-level leaf disease classification and precision agriculture systems.
Neha Sawant, K. L. Bansal· Indian Journal of Agricultur...· 0 citations
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