Jul 2026· 2026 4th International Conference on Sustainable Computing and Smart Systems (ICSCSS)· pp. 1404-1410· 0 citations· 8 references
Abstract
Early diagnosis of tree leaf diseases is crucial for ensuring ecological stability, conserving biodiversity and sustainable agriculture productivity. Manual inspection and traditional image processing methods are often subjective, time-consuming, and susceptible to environmental changes like illumination variations, complex background and orientation of leaves in the images, which makes them difficult to work with. In response to the above drawbacks, the present work aims at proposing a novel, intelligent deep learning-based framework for automated detection of tree leaf diseases and supporting tree Agri-knowledge, namely PHYTOASSIST. The proposed approach is based on YOLO (You Only Look Once) convolutional neural network (CNN) for real-time object recognition of disease region on diseased leaf using high resolution image. To ensure robustness and generalisation capabilities to field scenarios, image pre-processing is used such as resize, normalisation and augmentation. The framework also includes a fertilizer and treatment recommendation module as well as an interactive agricultural chatbot that offers contextually relevant information for agricultural practice and disease prevention. Experimental results have proven the robustness of the accuracy (96.2%), precision (95.4%), recall (94.8%), and F1 score (95.1%) with an inference speed of less than 10 frames per second. The outcomes demonstrate the effectiveness of the design of the framework supporting the scalable agricultural disease monitoring and intelligent decision support in sustainable agricultural environments.
This work introduces a deployable artificial intelligence system for automatic mango leaf disease recognition using deep transfer learning integrated with an interactive analytics dashboard and indicates that transfer learning can be trained efficiently and has a good predictive power.
Dr. Bhavana R Maale, Shivadarshini R· International Journal for Re...· 0 citations
AgriFusionNet is discussed, a context-aware multimodal deep learning framework that integrates leaf images, textual symptom descriptions, and environmental data for robust plant disease classification and facilitates the co-learning of visual, semantic, and contextual environmental representations.
V. C., Nischith N Shetty, M. Ramaiah et al.· Frontiers in Fungal Biology· 0 citations
Plant diseases are crucial for improving crop yield and ensuring sustainable agricultural practices, particularly for staple crops such as groundnut and paddy leaf. However, existing methods often suffer from limited feature discrimination, inadequate attention to disease-affected regions, and reduced performance under real-world conditions. To address these limitations, this research introduces a novel deep learning (DL)-based GOPI-NET framework for precise groundnut and paddy leaf disease classification. Initially, the input leaf images are enhanced using Bilateral Filtering (BF) and Contrast Limited Adaptive Histogram Equalization (CLAHE) to reduce noise and improve contrast. Subsequently, HSV color space segmentation is employed to precisely isolate disease-affected regions. The proposed Dual Attention Network (DuAtNet) integrates channel and spatial attention mechanisms within a ConvNeXt backbone to capture discriminative disease-specific features. An efficient Fuzzy Extreme Learning Machine (FELM) classifier is then utilized for final categorization into Healthy, Leaf Spot, Bacterial Wilt, and Leaf Blight classes. The effectiveness of the GOPI-NET is evaluated using precision, recall, specificity, accuracy, and F1-score. The experimental results demonstrate that GOPI-NET achieves an overall accuracy of 98.32%. The GOPI-NET improves classification accuracy by 1.29%, 1.40%, and 2.23% compared to GLDICCNN, DNN-CSA, and LeafNet respectively.
S. Sharmila, V. Jeyalakshmi· Scientific Reports· 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
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
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