Jul 2026· 2026 International Conference on Emerging Trends in Information, Communication & Systems (ICETICS)· pp. 1-6· 0 citations· 17 references
Abstract
Plant disease significantly influences agricultural production performance, particularly in valuable crops such as potatoes and tomatoes. The disease detection should be precise and automated in order to improve crop management and sustainable agriculture. Leaf disease classification for a number of diseases via multi-class approach using EfficientNetB3 architecture is proposed as the optimal structure of deep learning. Weights trained on ImageNet are used to facilitate transfer learning and feature extraction efficiency, while customized classifiers are designed to provide improved generalization, including batch normalization and dropout techniques. Input data diversity and robustness is provided via data augmentation techniques (rotation, translation, and horizontal flips). The adaptive learning approach together with Adamax optimizer are used to train the classifier. The classifier was tested using the PotatoTomato dataset from the PlantVillage repository which consists of six disease and healthy categories. The achieved accuracy score was 99.25%, while achieving precision and recall of various categories at 100%. The good performance in multi-classification proves the soundness of the classifier.
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.
Kuppala Ajay Kumar, Yella Sai Krishna, R. Kumar et al.· 2026 6th International Confe...· 0 citations
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.
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
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
Experimental data show that the proposed EfficientNetB0V2 + CNN model achieves superior performance, with higher accuracy and better precision, recall and F1 Score across all classes, highlighting the effectiveness of the suggested approach in detecting complex disease patterns.
Nisha Rani· International journal of com...· 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
