This study proposes a sugarcane leaf disease classification system using ConvNeXt V2 Tiny, a modern convolutional architecture with a Global Response Normalization mechanism, combined with an ensemble Stratified K-Fold Cross Validation strategy (K=6) to improve generalization on real-world field data.
Abstract
Sugarcane plant diseases pose a significant threat to agricultural productivity, yet early and accurate identification remains challenging for farmers due to the limitations of manual inspection. This study proposes a sugarcane leaf disease classification system using ConvNeXt V2 Tiny, a modern convolutional architecture with a Global Response Normalization (GRN) mechanism, combined with an ensemble Stratified K-Fold Cross Validation strategy (K=6) to improve generalization on real-world field data. A dataset of 2,948 leaf images spanning five classes (Red Rot, Mosaic, Rust, Yellow Leaf, and Healthy) was used, with field-collected images held out as a fixed test set. The ensemble model achieved a mean validation accuracy of 98.49% ± 0.58% across six folds and a test accuracy of 98.39% on 427 unseen field images, with macro-average precision, recall, and F1-score each reaching 98%. ConvNeXt V2 Tiny substantially outperformed ResNet-50 (87.35%) and EfficientNetV2-S (83.37%) under identical experimental settings, demonstrating superior generalization across the domain gap between curated and field data. The primary contribution of this study is the first application of ConvNeXt V2 Tiny with ensemble K-Fold strategy for sugarcane disease classification, offering high accuracy with moderate computational complexity (28.6M parameters) and practical deployability, as demonstrated through the SugarScan web application.
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
This study presents a novel CNN for multi-class classification of 38 diseases, demonstrating an effective balance between predictive performance and computational efficiency, positioning the model as a promising tool for real-world agricultural deployment.
Background: Faba bean is a nutritionally rich legume crop that contributes significantly to food security and sustainable agriculture. However, its productivity is adversely affected by foliar diseases such as chocolate spot, gall and rust. Accurate and timely disease identification under natural field conditions remains challenging due to environmental variability and visual similarity among symptoms. Lightweight deep learning models provide an efficient solution for automated and real-time disease diagnosis. Methods: This study proposed a MobileNet-based convolutional neural network trained from scratch for four-class classification of faba bean leaf images (Chocolate Spot, Gall, Rust and Healthy). A dataset of 8,021 RGB images collected under field conditions was divided using an 80:10:10 stratified split for training, validation and testing. Images were resized to 224x224 pixels, normalized to the range [0,1] and augmented through random rotation, flipping and zooming to enhance generalization. The model was trained using the Adam optimizer with a learning rate of 5x10-5 for 50 epochs with early stopping. Performance was evaluated using confusion matrix analysis, precision, recall, F1-score, ROC and PR curves. Result: The proposed model achieved 97.36% training accuracy and 96.32% validation accuracy, with a final test accuracy of 95.14% on 1,605 unseen samples. ROC curves demonstrated near-perfect separability with AUC values approaching 1.00, while class-wise metrics confirmed balanced performance. These findings indicate that the lightweight MobileNet architecture can effectively support reliable and scalable faba bean disease detection under real-world agricultural conditions.
Unknown authors· Legume Research An Internati...· 0 citations
This study presents a highly optimized, end-to-end deep learning pipeline leveraging transfer learning via the EfficientNet-B0 architecture for multi-class mango leaf disease classification, establishing a robust and computationally efficient baseline for automated precision pathology.
Jodell R. Bulaclac, J. D. Carmen· International Journal of Inn...· 0 citations
Early disease Classification will help reduce crop loss as well as increase agricultural productivity. A rapid and accurate deep learning-based framework to identify different diseases in eggplant on the marketplace level has been proposed in the research. Implementation and Training of an End-to-End Object Classification Based on YOLOv8. To train a custom multi-class data set, six target classes: Healthy Leaf, White Mold Disease, Leaf Spot Disease, Wilt Disease, Mosaic Virus Disease, and Insect Pest Disease. Initial work was done in developing the object detectors. We used standard metrics like accuracy, precision, recall, and F1-score to evaluate the performance of the model. When it came to finding Plant Leaf Disease (PLD), traceable configurations were revealed from configuration tuning among the combinations of hyperparameters, which converged at equal measurement intervals on a curve between Classification accuracy and computation efficiency from screened candidate architectures along ranges determined by performance metrics defined for detecting plant diseases using only above-ground debris as input sources. With stable convergence during the training phase, the model YOLOv8m had an accuracy of 96.84%, a precision of 96.85%, a recall of 96.84%, and an F1 score of 96.84%. The model that has been trained was deployed with the help of a web application named Streamlit, so that it could be used in practical procedures where disease can be detected if we upload an image. That means the system is robust and operates effectively in the wild as opposed to ideal test conditions, which lends itself well to agricultural usage. The present work provides an integrated, optimized deep learning detector with a user interface beneficial for precision farming that can help in the early identification of diseases on eggplants, resulting in increased yield.
Sujatha Krishna, O. I. Khalaf· International Journal of Dat...· 0 citations
Plant leaf diseases significantly reduce agricultural productivity and crop yield worldwide, making early and accurate detection essential to prevent large-scale crop damage. Traditional disease identification methods rely on manual inspection by experts, which is time-consuming, costly, and often inaccessible to farmers in rural areas. This paper proposes an AI-based leaf disease detection system using deep learning and transfer learning, in which EfficientNetB5 serves as a pretrained feature extractor to classify 38 plant disease categories spanning 14 crop species. Preprocessing includes HSV-based leaf segmentation, resizing to 456×456 pixels, and EfficientNet-specific normalization. A compact two-layer dense classifier is trained on the 2,048-dimensional feature vectors produced by the frozen backbone. The system achieves an overall validation accuracy of 96.49%, macro-average precision of 0.97, recall of 0.96, and F1-score of 0.96 on 2,280 held-out images. Beyond classification, the system provides actionable cure and precautionary recommendations for every detected disease, making it directly useful to smallholder farmers. Comparative analysis with ResNet50, VGG16, and MobileNetV2 confirms that EfficientNetB5 achieves the highest accuracy with a favorable parameter-to-performance ratio. Multi-class ROC evaluation further demonstrates strong discriminative capability across all disease categories.
Kuppala Ajay Kumar, Yella Sai Krishna, R. Kumar et al.· 2026 6th International Confe...· 0 citations