Jul 2026· 2026 International Conference on Emerging Trends in Information, Communication & Systems (ICETICS)· pp. 1-6· 0 citations· 22 references
Abstract
Farmers produce a lot of tomatoes, hence early identification and tomato leaf disease diagnosis can increase production. The information used in this research is public ally available tomato leaf image dataset with multiple disease classifications to create and improve the model. This work employs a methodical preparation workflow that includes picture scaling, normalization, and augmentation to increase resilience to variations in illumination, background clutter, and leaf texture. Two improved deep learning models, Residual and MobileNetV2, the processed pictures to enable efficient mining of significant spatial and texture information related to illness patterns. The experiment's results demonstrate the framework's utility and reliability: MobileNetV2 showed precision of 95.11, accuracy of 95.01, F1 of 95.00, and recall of 95.01 in contrast. The residual model showed 97.76 accuracy, 97.78 precision, 97.76 recall, and 97.64 F1. For every evaluation metric, these perform better than conventional DL architectures like VGG16, CNN, and ResNet. impacting early identification and improved agricultural performance.
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
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 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.
A hybrid framework integrating a Convolutional Neural Network with a Large Language Model to perform image-based plant leaf disease classification accompanied by interpretable diagnostic explanations demonstrated that combining high-performance visual recognition with language-based reasoning enhanced both diagnostic reliability and interpretability in digital agriculture applications.
Frenky Riski Gilang Pratama, S. Surono, A. Thobirin· International Journal of Adv...· 0 citations
With the global prevalence of tomato diseases causing 20 to 40% annual crop losses and over USD 220 billion in economic damage, traditional manual scouting and laboratory diagnostics prove labor intensive, subjective, delayed, and impractical for resource constrained rural farmers. To address this challenge, this study proposes a lightweight 17 layer convolutional neural network (CNN) model enhanced by comprehensive data augmentation, effectively classifying nine prevalent tomato leaf diseases Bacterial Spot, Early Blight, Late Blight, Leaf Mold, Septoria Leaf Spot, Spider Mites, Target Spot, Yellow Leaf Curl Virus, and Mosaic Virus using the PlantVillage dataset of 16,012 images. The experiment utilized 80/20 train test splits with Adam optimizer (learning rate 0.001), categorical cross entropy loss, 50 epochs, and batch size 32. The proposed CNN was compared with pretrained InceptionV3 and ResNet152V2 baselines. Experimental results demonstrate the model achieves state of the art performance with 95.28% test accuracy, 97.80% training accuracy, 0.970 macro F1 score, 0.932 micro MCC, and 0.983 micro average AUC, outperforming InceptionV3 (81.54%) and ResNet152V2 (85.89%) by 13.74% and 9.39% respectively, while surpassing tomato specific SOTA VGG 19 (93%). Ablation experiments confirm augmentation yields 16.68% accuracy improvement over non augmented baselines. The model powers a React Native Android app with TensorFlow Lite INT8 quantization (7.1 MB), delivering sub 200 ms inference for online cloud analysis via FastAPI and offline edge computing, providing farmers real time diagnostics with robust generalization across diverse field conditions and significant practical value for precision agriculture and food security.
D. M. Balungu, Maksim Aleksandrovich Malykh, Dmitry Evgenievich Burdin et al.· Informatica· 0 citations
A deep hybrid Convolutional Neural Network –Transformer architecture is introduced by combining ConvNeXt Large (ConvNeXt-L) and Swin Transformer architecture by combining ConvNeXt Large (ConvNeXt-L) and Swin Transformer (as local feature extractor) and Swin Transformer (as global context encoder) to predict tomato leaf diseases.
