Potato leaf disease poses a significant threat to global food security, causing substantial crop losses that jeopardise agricultural productivity and farmers’ livelihoods worldwide. Existing automated detection frameworks suffer from several persistent limitations, including over-reliance on controlled benchmark datasets, narrow disease class coverage, exclusive use of spatial feature representations, absence of feature selection, and dependence on single-architecture end-to-end pipelines. To address these limitations, this paper proposes ComPo-Net, a novel lightweight ensemble framework that integrates three efficient CNN architectures—ResNet18, ShuffleNet, and MobileNetV2—for nine-class potato leaf disease detection and classification. Deep features are extracted from three intermediate layers of each network, with the Discrete Wavelet Transform applied for dimensionality reduction and cross-network fusion of the higher-dimensional layer features, capturing spectral–spatial information that purely spatial approaches cannot provide, while the remaining layer features are directly concatenated across networks. One-way Analysis of Variance (ANOVA) feature selection is subsequently applied to retain the most statistically significant features from the combined multi-scale, multi-network representation, and seven machine learning classifiers are systematically evaluated to identify the optimal classification strategy. The framework is assessed on a merged dataset of three publicly available benchmarks spanning both controlled and uncontrolled imaging environments, constituting a nine-class evaluation setting not previously addressed at this scale in the literature. ComPo-Net achieves an accuracy of 96.32%, an F1-score of 93.87%, an MCC of 0.9350, and AUC values exceeding 0.993 across all nine classes with Cubic SVM as the best-performing classifier. When compared against methods evaluated on the seven-class uncontrolled-environment dataset—the closest available task setting to ComPo-Net’s nine-class merged benchmark—ComPo-Net surpasses the best-performing comparable method by a margin of 6.45 percentage points, demonstrating the effectiveness of multi-scale ensemble feature extraction combined with spectral–spatial representation and principled feature selection for robust potato leaf disease detection under diverse real-world conditions.
A unique, computationally efficient triple-feature block network capable of highly accurate plant disease classification across diverse species and complex imaging environments is proposed.
A. Elkholy, N. Elshennawy, Ahmed M. Gab Allah· Journal of King Saud Univers...· 0 citations
Crop diseases pose a substantial threat to global food security, yet existing detection methods often suffer from delays and labor-intensive processes. We present a comparative analysis of four transfer learning architectures—VGG16, ResNet50, MobileNetV2, and EfficientNetB0—for automated crop disease classification from leaf images. Our framework leverages a supervised deep learning approach trained on approximately 54,000 labeled images from six crop species (tomato, potato, maize, pepper, grapes, and apple) sourced from the PlantVillage database, covering seven major disease categories. All images were preprocessed to 224×224 pixels and augmented with rotation, flipping, zoom, and brightness adjustments. We employed a consistent architecture comprising a pretrained CNN feature extractor, global average pooling, a fully connected dense layer, and a softmax classifier. The Adam optimizer with a learning rate of 0.001 and categorical cross-entropy loss were used over 50 epochs with a batch size of 32. The dataset was split into 70% training, 15% validation, and 15% testing. Among all models, EfficientNetB0 achieved the highest accuracy of 97.3%, with precision, recall, and F1-score of 97.0%, 97.1%, and 97.0% respectively. ResNet50 attained 95.4% accuracy, MobileNetV2 reached 94.1%, and VGG16 yielded 93.2%. Confusion matrix analysis revealed minor misclassifications primarily among visually similar fungal diseases. Training curves demonstrated stable convergence with minimal overfitting. The superior performance of EfficientNetB0 highlights its suitability for real-time agricultural monitoring. This research contributes a systematic evaluation of transfer learning architectures for precision agriculture, demonstrating that pretrained models can effectively reduce computational complexity while maintaining high diagnostic accuracy.
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 existence of plant leaf diseases is a big problem for farmers all over the world because they make crops less healthy and less plentiful, which puts global food security at risk. The most common problems with diagnosing plant leaf diseases are a lack of experience, different ways of undertaking visual assessments, and image overlaps, all of which can lead to wrong diagnoses. This study introduces a hybrid deep learning architecture that integrates squeeze-and-excitation residual blocks, capsule networks, bidirectional long short-term memory, and attention mechanisms. The design utilizes convolutional operators for effective feature extraction, Squeeze-and-Excitation (SE) block for channel reweighting, capsule networks for spatial relationship capture Bidirectional Long Short-Term Memory (BiLSTM) for sequential dependencies, and attention mechanisms for emphasizing prominent features. Experiments were performed on 2 empirical datasets: the Corn Leaf Disease Dataset (CLDD) and the Rice Leaf Disease Dataset (RLDD). The data were divided into 60% for training, 20% for validation, and 20% for testing. The proposed method attained 99.88% training accuracy on CLDD and 100% on RLDD. During testing, the class-wise accuracies were 99.29% for blight and 100% for the other CLDD categories. In the case of RLDD, the accuracies attained were 78.95% for bacterial leaf blight, 85.53% for brown spot, 89.77% for healthy samples, 77.27% for leaf blast, 100% for leaf scald, and 97.73% for narrow brown spot. This work highlights practical potential for deployment in terms of mobile applications, enabling farmers to obtain rapid, reliable, and cost-effective field diagnoses, thereby improving agricultural productivity and sustainability.
Aekkarat Suksukont, Ekachai Naowanich· Journal of Advances in Infor...· 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
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.
Ashraf Mustafa, Araz Rajab Abrahim· Science Journal of Universit...· 0 citations