Jul 2026· 2026 International Conference on Emerging Trends in Information, Communication & Systems (ICETICS)· pp. 1-5· 0 citations· 12 references
Abstract
The infection of mango leaves is a major yield and fruit quality loss problem, and the necessity of having a precise and early diagnostic system for sustainable agriculture. Five convolutional neural network (CNN) architectures–namely, VGG16, VGG19, ResNet50, DenseNet121, and a custom-built AlexNet variant–were evaluated for their performance in classifying eight categories of mango leaf diseases. All models were pre-trained using a well-defined dataset of healthy and infected leaves and fine-tuned using transfer learning. VGG16 had the highest test accuracy of 99%, which was better than ResNet50 (91.5%), DenseNet121 (90.5%), VGG19 (87%), and AlexNet (86.5%). Further evaluation of these models using confusion matrices and F1-scores was conducted to ensure the strength of these models for various diseases including powdery mildew, anthracnose, and bacterial canker. Overall, the results underscore that advanced CNN models—particularly VGG16— can deliver near-expert precision in automated mango leaf disease identification. The leaf disease of mango is a major issue confronting farmers in Southeast Asia, particularly in India. Mango leaf disease is one of the biggest challenges faced by the farmers of south-east Asian countries, especially India.
An extensive set of experiments was conducted to evaluate the performance of the proposed model for plant disease detection, and it is demonstrated that the model achieves highly reliable results, with an accuracy of 97.13%.
Hayat Meddeber, M. Meddeber· ITEGAM- Journal of Engineeri...· 0 citations
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.
Ranu Solanki, D. Yadav· International Journal For Mu...· 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
Objectives: To create a multi-class image classification system to automate the detection of potato crop diseases using deep learning algorithms to classify images of potato leaves. Method: This study involves an implementing and comparing of six deep learning models to classify potato leaves as diseased or infected with pests. The models included a custom CNN as the baseline and five transfer-learning models: VGG16, DenseNet121, MobileNetV2, Xception, and InceptionV3. The final selected model was InceptionV3 due to its ability to extract strong features and achieve superior overall classification performance among all evaluated models. To enhance model’s performance and improve generalization to unseen data, several techniques were implemented, including data augmentation, Batch Normalization, Dropout regularization, and selective fine-tuning of deeper layers. Findings: The proposed model achieved the highest test accuracy (94%) and macro-average F1-score (0.94) compared to other baseline models. The importance of fine-tuning is reflected in the high accuracy of the proposed model. An ablation study found that accuracy dropped to 84.67% without fine-tuning, which demonstrates how critical it is for this model’s domain adaptation. The Grad-CAM analysis showed that the model focuses on biologically relevant areas of the leaves with infection and does not concentrate on backgrounds; therefore, the results indicate the model’s potential for interpretability and deployment in real-world settings. Novelty: This study improves potato leaf disease detection using a fine-tuned InceptionV3 with data augmentation and dropout, while Grad-CAM visualizations enhance model interpretability, reliability, and practical utility for accurate agricultural disease diagnosis.
Keywords: PotatoLeaf Disease Detection, Deep Learning, Transfer Learning, InceptionV3, Image Classification, Grad-CAM, Sustainable Agriculture
Aradhy Tiwari, Amit Saxena, Chandrashekhar Chandrashekhar· Indian Journal of Science an...· 0 citations
Strawberry is highly prone to numerous foliar diseases which can drastically decrease the yield and quality unless well identified. The conventional ways of disease identification are based on manual inspection, which is very tedious and ineffective in large farms. The creation of artificial intelligence (AI) has enabled the application of deep learning (DL) models to identify illnesses in plants through the examination of images automatically. This paper will give a detailed comparison and analysis of various deep learning models used to diagnose and classify strawberry leaf diseases. A collection of images of normal and diseased strawberry leaves is used with a number of more advanced convolutional neural network (CNN) models, such as VGG16, ResNet50, and InceptionV3, and transfer learning is used to use the knowledge gained with pre-trained models and simplify the training process. The simulated results indicate that each of the models is a reasonable classification model, with InceptionV3 being the most accurate and VGG16 being the best tradeoff between accuracy and resource-efficiency, so it is applicable in real-time and resource-constrained conditions.
S. R· 2026 4th International Confe...· 0 citations
This study presents a novel CNN for multi-class classification of 38 diseases, demonstrating an effective balance between predictive performance and computational efficiency, positioning the model as a promising tool for real-world agricultural deployment.