Jul 2026· Science Journal of University of Zakho· 0 citations
TL;DR
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.
Abstract
Grapevines are economically vital crops but are highly susceptible to fungal, bacterial, and viral diseases that threaten yield and quality. Traditional detection methods rely on manual inspection, are time-consuming, prone to human error, and often delay intervention. This study introduces a lightweight convolutional neural network (CNN) architecture specifically designed for accurately and efficiently detecting grapevine leaf diseases—including Black Rot, ESCA, and Leaf Blight—based on image classification. The proposed model integrates optimized residual blocks, batch normalization, dropout layers, and global average pooling to maximize accuracy while minimizing computational complexity. This lightweight design makes it well-suited for deployment on edge devices such as drones and mobile systems used in precision agriculture. A comprehensive data augmentation strategy was applied during training to simulate real-world variability and enhance generalization. The model was trained using 9,027 labeled grape leaf images from a publicly available grape disease image dataset, and it achieved 99.8% overall accuracy with near perfict precision, recall, F1-score, and area under the ROC curve (AUC) across all classes. These findings highlight the practical potential or real-time, scalable and sustainable disease monitoring in smart vineyard management systems.
Citrus crops are economically vital worldwide, yet they remain highly susceptible to a range of infectious diseases that cause considerable yield and quality losses each year. Early and accurate disease identification is fundamental to sustainable orchard management and food security. Over the past decade, deep learning has emerged as the dominant paradigm for automated plant disease detection, surpassing traditional image-processing pipelines in both accuracy and scalability. This paper presents a comprehensive review of deep learning methodologies applied to citrus disease detection, covering convolutional neural networks (CNNs), attention mechanisms, lightweight architectures, object detection frameworks, multimodal fusion, and edge-computing deployment. Recent studies are critically analyzed with respect to model architecture, dataset characteristics, performance metrics, and deployment context. The review identifies prevailing trends including the shift toward lightweight models for edge devices, the integration of attention modules for fine-grained feature capture, and the growing adoption of multimodal and transformer-based approaches. Key open challenges such as limited data diversity, computational constraints in field deployments, and the need for domain-adaptive models are also discussed, along with prospective research directions. The findings serve as a reference for researchers and practitioners seeking to develop robust, real-time citrus disease detection systems.
Aniket K. Shahade, Vishal Jain, G. Manteghi et al.· 2026 International Conferenc...· 0 citations
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.
Ammar Kamil Al Abror, Melika Debiyana Putri, Yunanda Rizki Sitompul et al.· bit-Tech· 0 citations
Grapevine diseases represent a major threat to vineyard productivity, with Black Rot being among the most destructive due to its rapid spread and visual similarity to other diseases. These diseases are associated with a diversity of pathogenic agents, namely fungi, oomycetes, bacteria and pests. While prior work frequently reports high accuracy in controlled multi-class classification, practical deployments commonly require selective detection of a target disease against a contaminated negative class. In this work, Black Rot detection is formulated as a binary classification task, where the negative class includes healthy leaves and other visually similar diseases. This study employed ImageNet pretrained Convolutional Neural Network (CNN) backbones, MobileNetV2, DenseNet121, ResNet50 and VGG16, using a two-stage transfer learning protocol. The ability of the CNN models to accurately identify Black Rot cases was evaluated using standard classification metrics, namely accuracy, precision, recall and F1-score. The results show clear differences in detection behaviour across architectures. ResNet50 achieves the highest overall performance, obtaining 100.0% precision, with no false positives while maintaining a high recall 96.3% and a F1-score of 98.1%, with an accuracy of 98.9%. Overall, the achieved performance is competitive and exceeds values reported in the literature, while addressing a more realistic contaminated-negative scenario.
Patrícia Sergueevna Moltchanova, Luís Nero Alves, C. Ferreira· International Symposium on C...· 0 citations
Tomato leaf diseases must be identified early and accurately in order to reduce output loss and advance sustainable agriculture. Deep learning models have shown encouraging results in the identification of plant diseases, but their high processing requirements and inability to adjust to field-specific limitations sometimes make it difficult to implement them in real-world applications. For the purpose of accurately and efficiently classifying tomato leaf diseases, we present a convolutional neural network based on AlexNet that is lightweight and field-aware. Our proposed model is designed with less complexity than traditional architectures and is able to perform inference faster without sacrificing accuracy, making it suitable for real-time implementation in low resource agricultural environments. The algorithm was trained and tested on a dataset of 7704 augmented images of tomato leaves from 7 different disease categories. The Lightweight AlexNet achieved accuracy comparable to VGG variants and outperformed deeper models such as ResNet (96.81%) with lower parameter overhead. It was trained from scratch using TensorFlow & Keras on 100×100 pixel inputs, achieving a training accuracy of 99.15% and a validation accuracy of 99.74%. Moreover, an effective structure of the model enables the installation on edge devices, which offers a scalable precision farming solution. Our work helps to bridge the gap between deep learning research and real-world application in agriculture, allowing the development of real-field, resource-efficient disease detection systems.
Debabrat Bharal, Kanak Ch Bora, Sailen Dutta Kalita· International Research Journ...· 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
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