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 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
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
Agriculture is a key component in ensuring food security in the world and one of the most common staple foods in the world is rice. But there are a number of leaf diseases affecting rice, like bacterial leaf blight, brown spot, and rice blast, which cause lower production yield. In order to limit economic losses, it is necessary to be able to diagnose these diseases on time. This paper presents an intelligent prediction and classification of rice leaf disease prediction and classification using deep learning and transfer learning approaches. For that purpose, the proposed model uses two pre-trained transfer learning models, DenseNet201 and EfficientNetB3, and a conventional CNN, to classify healthy and diseased rice leaves from a set of image data. The performances of the three models are measured using the following performance measures: accuracy, precision, recall, and F1-score. From the experiments conducted, it can be noted that the best performing model among all three models is the EfficientNetB3, which shows an excellent classification accuracy of 95%. It performs better than both DenseNet201, whose accuracy is 92% and CNN model, whose accuracy is 83.5%. The reason behind the excellent performance of the EfficientNetB3 is that the architecture of the model is optimized in such a way that it is able to extract the most discriminative features with less computational complexity. The proposed framework can offer an efficient, reliable, and automated solution for the early detection of the diseases in the leaves of rice crops.
Vandana Samanthula, N. Valli, Sultanov Sirojiddin et al.· 2026 6th International Confe...· 0 citations
Plant diseases are among the most significant challenges in agriculture because they reduce crop productivity and cause economic losses to farmers. Early monitoring of plant diseases is important for controlling infection spread and improving crop management. Traditional plant disease detection techniques depend heavily on manual inspection by agronomists; therefore, they are time-consuming, labor-intensive, subjective, and often inaccessible to farmers in remote locations. In recent years, plant disease classification has been performed using deep learning architectures such as CNN, ResNet, MobileNet, and EfficientNet. Although these models can be accurate, many of them require higher computational resources and usually focus only on disease detection without providing a practical treatment recommendation. This paper proposes a smart crop health monitoring system for plant leaf disease classification and pesticide advisory using the Ultralytics YOLO26s-cls model. The proposed system is trained on the New Plant Diseases Dataset containing 87,867 plant leaf images from 14 crop species and 38 disease/healthy classes. The pipeline includes image preprocessing, augmentation, YOLO26s-cls-based feature extraction, disease classification, and a validated rule-based pesticide recommendation module. The model architecture, model selection rationale, overfitting-control strategy, dataset validation procedure, and pesticide-rule validation process are explicitly described to support reproducibility. The proposed classifier achieved 99.02% validation accuracy, 99.01% macro precision, 99.00% macro recall, and 99.00% macro F1-score. The system can therefore serve as a decision-support tool for early disease identification and pesticide selection in precision agriculture.
Radha Krishna Mukkapati, Jayendra Nelakurthi, P. R. et al.· International Conference Com...· 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.
Plant leaf diseases are known to affect agricultural productivity and food security on a global level. "Therefore, the detection and diagnosis of diseases are important aspects of maintaining the health of crops on a sustainable level. Traditionally, the detection of diseases in plants is performed manually by experts. This process is considered to be a tedious and time-consuming task due to the chances of human error during the process. To overcome the challenges of traditional methods of detecting and diagnosing diseases in plants, a deep learning-based system is proposed in this research study to detect and classify diseases in plants. This system uses the object detection model YOLOv8x and YOLOv10x to detect the objects in the images and classify the images accordingly. Deep learning models are used to classify the images of the plants. This study uses various deep learning models like the convolutional neural network model ResNet50 and EfficientNet, and the transformer model Vision Transformer and Swin Transformer. Moreover, a hybrid model is proposed in this study by combining the transformer and convolutional neural network model to improve the efficiency of the system in detecting and classifying the diseases of the plants. This system uses the PlantVillage dataset to classify the images of the plants and detect the diseases accordingly. From the results obtained in this study, it can be observed that the proposed system is highly efficient in detecting and classifying the diseases of the plants with the help of the transformer and hybrid model.