Jul 2026· International Journal of Innovative Science and Research Technology· 0 citations· 13 references
TL;DR
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.
Abstract
Phytopathological threats to mango (Mangifera indica L.) cultivation cause severe global agricultural yield
losses, necessitating rapid and accurate diagnostic frameworks. 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. The model was trained and rigorously evaluated on the MangoLeafBD dataset, comprising 4,000 balanced
images across eight categorical states (one healthy control and seven distinct fungal and bacterial pathologies) captured
under heterogeneous orchard conditions. Utilizing a stratified 70-15-15 data split, the network achieved exceptional
convergence within 16 epochs. Empirical evaluation on an isolated test set of 600 images yielded an absolute classification
accuracy of 100 percent, with precision, recall, and F1-scores of 1.0 across all classes and zero off-diagonal
misclassifications. Furthermore, the pipeline demonstrated near-zero latency inference on standard edge-computing
hardware. These findings validate the deployment viability of lightweight convolutional neural networks in resourceconstrained agricultural environments, establishing a robust and computationally efficient baseline for automated
precision pathology.
India is one of the biggest producers and exporters of mangoes in the world, yet its cultivation is persistently threatened diseases that reduce yield, fruit quality, and orchard longevity. Traditional disease diagnosis is based on agronomists' hand visual inspection, which is a laborious, subjective, and challenging technique to scale across vast plantations. This research provides a hybrid deep learning system that incorporates AlexNet and ResNet-50 for the automated classification of five commercially relevant mango leaf diseases: Bacterial Canker, Anthracnose, Powdery Mildew, Sooty Mould and Healthy foliage. Through a fused, jointly trained classification head, the suggested architecture combines the deep, residual feature hierarchies of ResNet-50 with the shallow, texture-sensitive representations learned by AlexNet, enabling the network to take advantage of complementary visual cues that neither backbone fully captures on its own. The hybrid model was implemented and trained using MATLAB. The trained model achieved a validation accuracy of 99.47%. Comparative analysis against standalone AlexNet, standalone ResNet-50, and other architectures reported in the recent mango plant-disease literature indicates that the hybrid fusion strategy offers a favourable balance of accuracy and convergence stability.
Ranu Solanki, D. Yadav· International Journal For Mu...· 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.
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
The infection of mango leaves is a major yield and fruit quality loss problem, and the necessity of having a precise and early diagnostic system for sustainable agriculture. Five convolutional neural network (CNN) architectures–namely, VGG16, VGG19, ResNet50, DenseNet121, and a custom-built AlexNet variant–were evaluated for their performance in classifying eight categories of mango leaf diseases. All models were pre-trained using a well-defined dataset of healthy and infected leaves and fine-tuned using transfer learning. VGG16 had the highest test accuracy of 99%, which was better than ResNet50 (91.5%), DenseNet121 (90.5%), VGG19 (87%), and AlexNet (86.5%). Further evaluation of these models using confusion matrices and F1-scores was conducted to ensure the strength of these models for various diseases including powdery mildew, anthracnose, and bacterial canker. Overall, the results underscore that advanced CNN models—particularly VGG16— can deliver near-expert precision in automated mango leaf disease identification. The leaf disease of mango is a major issue confronting farmers in Southeast Asia, particularly in India. Mango leaf disease is one of the biggest challenges faced by the farmers of south-east Asian countries, especially India.
P. G. K., Kiran Kumar H R· 2026 International Conferenc...· 0 citations
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.
This study introduces a lightweight convolutional neural network architecture specifically designed for accurately and efficiently detecting grapevine leaf diseases—including Black Rot, ESCA, and Leaf Blight—based on image classification.
Ashraf Mustafa, Araz Rajab Abrahim· Science Journal of Universit...· 0 citations