Real-Time Pomegranate Leaf Disease Detection Using Deep Learning Models

GRAPEVINE DISEASE DETECTION USING AN OPTIMIZED DEEP NEURAL NETWORK FOR SMART AGRICULTURE

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.

Deep Learning-based Classification of Faba Bean Leaf Diseases using MobileNet Trained from Scratch

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.

AI Enabled Early-Stage Brinjal Leaf Disease Detection using Pre-Trained Models

To correctly distinguish leaf diseases in eggplant (Solanum melongena), it is very important to improve agricultural production systems for precision. This paper presents a comparative analysis of five pre-trained convolutional neural network models, namely ResNet50, MobileNetV2, GoogLeNet, Xception, and VGG16, for multi-class classification of eggplant leaf diseases. In this work, a composite image dataset was developed by combining images from publicly available Kaggle , Mendeley repositories and local dataset (by collection from fields) to improve data diversity and generalization capability. A structurally optimized VGG16 model was developed to process 128 × 128 pixel images, which aimed to reduce computational complexity with preserved classification accuracy. Under the same training environment, the proposed modification achieved a classification accuracy of 96.5%. In addition to quantitative analysis, interpretability of the model was incorporated using Local Interpretable Model-agnostic Explanations (LIME) to generate localized feature attribution maps, thereby addressing the transparency problem associated with deep neural networks. The experimental results demonstrated that, although ResNet50 achieved the highest overall classification accuracy, the modified VGG16 architecture demonstrated a more balanced performance in terms of computational complexity, latency, and interpretability. Thus, the modified VGG16 is a reasonable candidate for use in real-time resource-constrained agricultural disease diagnostic system applications.

Implementation of MobileNetV2-Based Deep Learning for Corn Leaf Disease Detection Using a Web-Based System

The primary contribution of this work lies in the empirical demonstration that MobileNetV2, without architectural modification, can serve as a practical and accessible diagnostic tool when integrated into a web-based deployment pipeline, offering a favorable trade-off between accuracy and computational cost compared to heavier architectures.

KashWalNET: Efficient Walnut Disease Recognition Using K-Means-Guided Segmentation and CNN

Plant Leaf Disease Detection using EfficientNetB5 and Transfer Learning

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.