A hybrid ResNet-ViT and YOLOv3-ViT pipeline for interpretable mango leaf disease diagnosis

Hybrid Deep Learning Architectures for Automated Mango Leaf Disease Detection

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.

Hybrid CNN and LLM for Image-Based Classification of Plant Leaf Diseases

A hybrid framework integrating a Convolutional Neural Network with a Large Language Model to perform image-based plant leaf disease classification accompanied by interpretable diagnostic explanations demonstrated that combining high-performance visual recognition with language-based reasoning enhanced both diagnostic reliability and interpretability in digital agriculture applications.

REDNet: An Explainable Deep Learning Framework for Mango Leaf Disease Classification

EfficientNet-CBAM-prototype: an attention-guided EfficientNet framework with dynamic prototype representation for tomato disease classification

In the field of precision agriculture, one of the major hurdles is the early and accurate identification of plant diseases. Farmers may face serious irreversible loss in yield if there is a delay in diagnosis by even a few days. The CNN model has helped in improving the classification of plant diseases but faces difficulty in distinguishing fine-grained symptoms, has limited generalizability, and is not easily interpretable. Though the deep CNN model performs well in classifying plant diseases, the current methods have several flaws. Most importantly, existing algorithms misclassify visually complex samples due to their limited spatial differentiation of disease-specific morphological features, including lesion borders and necrotic regions. Classification reliability is further compromised by low inter-class embedding separability for visually comparable illness phenotypes. Apart from these representational problems, training instability is still a major problem for attention-based models. Combining randomly initialized attention modules with pretrained backbone networks causes this instability, which still limits practical application. We suggest EfficientNet-CBAM-Prototype (ECP-Net), a unique end-to-end deep learning architecture, to overcome these constraints. Three complementary techniques are combined into a single framework by ECP-Net. First, parameter-efficient multi-scale feature extraction is done using an EfficientNetB0 backbone. Second, joint channel-wise and spatial feature recalibration is performed using a stabilized convolutional block attention module (CBAM). Third, a dynamic prototype memory layer uses cosine similarity-based categorization and exponential moving average (EMA) updates to maintain class-representative embedding vectors. To overcome the instability caused by randomly initialized attention weights, we further propose a two-phase training strategy wherein CBAM is frozen during phase 1 to allow prototype stabilization and then jointly fine-tuned with the learning rate in phase 2. Evaluated on the PlantVillage tomato subset comprising 10 disease classes across a class-balanced split of 10,000 training, 500 validation, and 500 test samples, ECP-Net achieves 98.6% test accuracy, 98.59% F1-score, and 98.65% precision with only 4.80M parameters and 85.04 ms average inference time. These results outperformed baselines including VGG16 (97.00%), ResNet50 (81.20%), MobileNetV2 (81.20%), and CNN (70.00%). Generalization is further validated on 35 real-field tomato leaf images captured under natural, uncontrolled conditions, confirming practical deployment potential.

SmartAgro-ViT: A Self-Supervised Explainable AI Framework for Plant Disease Analysis

In order to avoid a global food shortage and maximise agricultural production, rapid and accurate detection of plant diseases is essential. Although image-based plant disease recognition has been enhanced by deep learning, the majority of these methods rely on massive annotated datasets and employ black-box models, rendering them ill-suited for usage in agricultural contexts. The Future of SmartAgro-ViT An AI system that uses transformers to analyse plant diseases can be self-supervised and explained. To lessen the need for human annotation, the suggested method employs self-supervised pretraining to learn detailed visual representations of plants from large datasets of unlabelled images. A fine-tuning process is necessary for supervised disease classification across different crop species. An explainability module makes the model more accessible by providing visual attention maps that emphasise areas of plant leaves that are related to diseases. This helps in making accurate predictions. In comprehensive studies conducted on benchmark plant disease datasets, SmartAgro-ViT demonstrated superior classification accuracy and robustness compared to supervised transformer and convolutional neural network models. Various lighting and backgrounds complement the structure. The intelligent plant disease diagnosis capabilities of SmartAgro-ViT are useful for precision agriculture and smart farming systems since they are both effective and easy to understand.

Cbam-augmented ResNet for high-accuracy grape leaf disease detection