Jul 2026· 2026 6th International Conference on Inventive Computation and Information Technologies (ICICIT)· pp. 1148-1155· 0 citations· 22 references
Abstract
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.
The proposed automated leaf disease detection system using image processing and deep learning techniques can detect leaf diseases effectively and efficiently, making it a useful and cost-effective solution for supporting farmers and agricultural experts in early disease diagnosis.
Shilpa T S, K. U, A. Jajur. J· World Journal of Advanced En...· 0 citations
Understanding the DL models suggested for plant leaf disease detection and classification using the YOLO principle is the primary goal of this survey and provides a comparative and performance analysis of these models by examining their techniques, merits, demerits, datasets used, and evaluation metrics.
K. Subhashini, M. Vijayakumar· International Journal of Sci...· 0 citations
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.
Gaurav Tuteja, Syed Nawaz Pasha, Tushar Sharma et al.· 2026 International Conferenc...· 0 citations
The Plant Disease Detecting System leverages advances in artificial intelligence and deep learning to provide an automated, efficient, and reliable solution for identifying plant diseases at an early stage and contributes to increased crop productivity, reduced chemical usage, and sustainable farming practices.
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.
The system employed a convolutional neural network/transfer learning model to identify eggplant leaf diseases accurately and efficiently and was integrated into a web-based application that allows users to upload leaf images and obtain real-time diagnostic results along with recommended handling information.
Abu Tholib, Moh. Ainol Yaqin· JOKI: Journal of Computing a...· 0 citations