Jul 2026· VFAST Transactions on Software Engineering· Vol 14, pp. 61-73· 0 citations· 18 references
TL;DR
There is a large potential to combine image analysis with deep learning-based feature extraction and intelligent classification algorithms to achieve an earlier disease diagnosis of apple production.
Abstract
Apple fruit has major significance in the global agricultural economy due to the nutritional value that it provides and its economic benefits. In addition, there are many threats to UK apple production from various diseases, including foliar disease. There are several different methods being developed to diagnose plant disease. To overcome these limitations, this paper proposes an automated system for the identification and classification of diseased regions on apple fruit leaves using the MNTV3-ECOC architecture. The first step in the process is to apply Bilateral Filtering to remove noise from the images while preserving leaf vein edges, and (CLAHE) was used to enhance contrast. Then, a SegNet model was utilized to automatically identify the region(s) affected by disease, and after that, a pre-trained version of the MobileNetV3 model was utilized to extract relevant features. Finally, an Error Correcting Output Codes (ECOC) model was used to classify the type of the disease. An experiment was conducted to test the ability of the proposed model using a publicly available dataset consisting of 13,124 images of apple fruit leaves, each classified into one of four disease types. The proposed model achieved a classification accuracy of 97.80% along with a precision of 97.38%, a sensitivity of 97.25%, a specificity of 97.60%, and an F-score of 97.50%. Thus, based on these findings there is a large potential to combine image analysis with deep learning-based feature extraction and intelligent classification algorithms to achieve an earlier disease diagnosis of apple production.
Agricultural productivity and food security are heavily impacted by plant diseases, and thus there is a high demand for accurate and automated plant disease detection that can be achieved by applying deep learning techniques. This research proposes a Multi-Model Ensemble Method Based on Deep Learning for multi-plant disease detection using ResNet50 to improve classification performance across multiple crop varieties. The proposed framework takes five important categories of plants into consideration including tomato, potato, grape, apple and maize, and 10 classes of healthy and diseased plants are generated from the PlantSeg dataset. The Anaconda platform was used along with Python to create a development environment that allows data preprocessing, augmentation, training and testing to be implemented efficiently. The proposed ensemble framework combines the feature extraction power of ResNet50 with several deep learning classifiers so as to obtain a good identification performance at different resolutions and environments. The proposed model performance is tested with the following metrics Accuracy, Precision, Inference Time, and Resolution quality and compared with MobileNetV2, YOLOv8 and the baseline CNN models. Experimental results show that the proposed ensemble ResNet50 framework achieves an accuracy of 98.7% and precision of 98.3%, which is about 6.4%, 4.8%, and 9.2% higher than that of MobileNetV2, YOLOv8, and CNN respectively. Moreover, the proposed method achieves high resolution disease localization capability with an inference time improvement of almost 18% compared with YOLOv8. The proposed system greatly improves the detection accuracy of the early stage and the calculation speed of the system, which is very suitable for smart agriculture applications and real-time monitoring of the health status of crops.
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.
Unknown authors· Legume Research An Internati...· 0 citations
Plant diseases have long been considered a major threat to global food production systems. Therefore, early diagnosis is vital to mitigate the risk of these diseases. This task can be challenging, as the number of harmful diseases is substantial. One technology that has gained widespread interest is artificial intelligence, specifically deep learning, which is used to identify plant diseases using leaf patterns. This paper presents two deep learning models, a custom CNN model and a transfer learning model based on the DenseNet-121 architecture. Experiments were carried out using the PlantCity dataset, which consists of twelve subsets of diverse crop species representing fruits, vegetables, and grains with variations among the subsets, including the number of classes, the subset sizes, class distribution, and visual complexity of disease symptoms. The two models were evaluated using multiple metrics, including accuracy, loss, precision, recall, and F1-score. Explainable AI using the LIME technique was deployed to better interpret the acquired results. Results showed that the developed transfer learning model based on DenseNet-121 had superior performance over the CNN model, with accuracies ranging from 86% to 99% across eleven experimented crops. In order to perform an independent experimental validation for the developed model, future work will focus on constructing a local crop dataset captured from Iraqi fields to evaluate the developed models based on the local environment.
H. Jeiad, S. Samaan, Omar Janeh et al.· Automation· 0 citations
The need to enhance crop yield, mitigate losses and ensure sustainable viticulture requires that grapevine diseases be identified accurately and at an early stage. In this paper, an effective deep learning based grapevine leaf classification framework with a fine-tuned MobileNetV3Large model is proposed. The data set used for the experiment was an image data set of 500 grapevine leaves, which included 5 different categories of Buzgulu, Ala Idris, Nazli, Dimnit, and Ak. Numerous preprocessing techniques have been used on the dataset, such as resizing of the image, image normalization, and data augmentation to improve the robustness and generalization of the model. The method of transfer learning was applied by taking MobileNetV3Large pre-trained on the ImageNet dataset and then training the layers that perform multi-class classification. Finally, the model was tested based on the following performance measures; accuracy, precision, recall, F1 score, and confusion matrix. According to the results obtained from the experiment, the precision rate was 96%, and the recall rates were 100%. The convergence and prevention of overfitting by early stopping is effectively indicated by training and validation performance curves. The results demonstrate the appropriateness of deep learning lightweight structures to real-world farming tasks and verify the possibility of the offered model as a dependable instrument to automatize the classification of grapevine diseases and aid precision farming and sustainable vineyard management.
Seerat Singla, S. Naresh, Nishant Bhandari et al.· 2026 International Conferenc...· 0 citations
Agricultural production is exposed to losses when plants became infected with diseases from the environment, which in turn threatens food security; therefore, having the ability to detect plant diseases early on gives farmers the opportunity to minimize the loss they incur. Sunflower (Helianthus annuus) is Ranking among other oilseed crops worldwide for total agricultural production, and it has a high risk of being affected by diseases that cause reduced crop yield and quality, e.g., Downy mildew, grey mould, and leaf scars. Disease detection using conventional methods usually involves visual inspections by trained personnel; this process, however, is very time consuming, subjective, and could produce numerous errors. To counteract these issues, this paper proposes a hybrid learning framework for plant disease detection that combines deep feature-driven image analysis with a combination of two classifiers used in an ensemble mode to create a single final classification. The use of MobileNetV2 will act as the feature extraction process for the hybrid learning framework, while the classifier portion of the hybrid learning framework will consist of two K-Nearest Neighbour classifiers. Classification using SVM and RF classifiers will be completed using a combination of ensemble voting. Grad-CAM (gradient-weighted class activation maps) will be used to improve the interpretability of the disease classification results by identifying affected areas on images. Test results for the hybrid learning framework reveal an overall accuracy of 98.8%, compared to the accuracy of the two classifiers used separately: 95.8% for CNN, 93% for SVM, and 96.0% for RF. The confusion matrix for the two classifiers shows a significant number of accurate classifications with minimal misclassifications. The proposed hybrid learning framework for plant disease detection provides an effective method for detecting plant diseases in real time, interpretable results, and potential for scalability.
Kshirsagar Soumya, Mr. G Sekhar Reddy, D. G. L. A. Babu et al.· International Conference Com...· 0 citations
The proposed Sugarcane Leaf Disease Detection and Classification System provides a fast, accurate, and user-friendly solution for automated disease diagnosis and contributes to improved crop management, reduced crop losses, and enhanced agricultural productivity.