TL;DR
An advanced Rice Disease Detection model incorporating a multi-step approach of image augmentation, preprocessing, feature extraction, and hybrid detection is introduced, addressing both computational efficiency and detection precision.
Rice is one of the most significant crops in all the world agriculture, diseases and pests of rice seriously threaten grain production. Identifying and classifying them accurately in the field is essential for prevention. However, the existing models often encounter issues such as unequal between lightweight deployment and recognition performance, weak robustness to various lighting conditions and insufficient generalization ability due to class imbalance. To address this issue, the paper proposed a lightweight method for rice diseases' classification based on the optimized MobileNetV2. Its core structure adopted MobileNetV2 as the backbone to retain the advantage of depthwise separable convolution lightweight model and added an SE attention layer after feature extraction stage to enhance the model's focus on lesion regions. Using Test-time- augmentation (TTA) further improves the model's robustness to lighting and shooting angle interference. Based on the experiment results, the proposed model achieves a test average accuracy of 83.9% and a maximum validation accuracy of 88.0% on the testing sets. Under extreme lighting conditions, the performance loss percentage is controlled at 20.78%. The rice disease identification method proposed in this paper provides a reference for the deployment and application of the method in different light conditions in the field.
- Enhancing agricultural productivity and attaining sustainable crop management depend on the early and precise identification of leaf disease. Using state-of-the-art technologies in precision agriculture like machine learning (ML) and image processing greatly increases the effectiveness of disease detection and facilitates well-informed decision-making. But conventional manual inspection techniques are still tedious, unpredictable, and prone to errors. In order to overcome these constraints, this research offers YOLOv12-CropNet, an innovative deep learning-based system for multi-crop leaf disease diagnosis in real time. The proposed YOLOv12-CropNet approach makes use of the Convolutional Block Attention Module (CBAM) for adaptive attention, the Content-Aware Reassembly of Features (CARAFE) up-sampling module to preserve fine-grained disease characteristics, the YOLOv12 architecture improved with Ghost Convolution for effective feature extraction, and Involution layers to capture spatially specific patterns. Inspection techniques are still laborious, arbitrary, and prone to mistakes. A substantial set of data of 38 classes of both healthy and sick leaves from a variety of crops, including tomato, potato, apple, grape, corn, mango and sugarcane, was put together for training and evaluation. Experimental results show that YOLOv12-CropNet finds a suitable balance between computational speed and accurate detection. Accuracy, F1-score, recall, and precision are important performance metrics that verify the model's resilience in challenging environmental and visual circumstances. The suggested technique provides a scalable and field-deployable way to assist effective identification of diseases and precision agricultural decision-making. The proposed YOLOv12-CropNet model exhibits better performance than the other evaluated models, attaining a 98.45% peak accuracy, 98.10% precision ,98.20 % sensitivity and a 98.18% F1 score, thereby highlighting its efficacy in multi-crop leaf disease detection.
Artificial intelligence has become an effective tool for improving agricultural productivity through automated crop disease diagnosis. Brinjal (eggplant) cultivation suffers substantial yield losses from diseases such as Shoot and Fruit Borer, Wet Rot, Fruit Cracking, and Phomopsis Blight, yet reliable field-based diagnostic systems remain limited. To address this challenge, we introduce \textit{BrinjalFruitX}, a real-world dataset comprising 1,823 annotated images collected under natural farming conditions in Bangladesh across four disease classes and one healthy class. We propose an interpretable hybrid feature-enhancement deep ensemble framework that integrates image preprocessing, transfer learning, traditional machine learning, class imbalance mitigation, and explainable artificial intelligence. Three preprocessing techniques, Gaussian, Laplacian, and Unsharp Masking, are systematically evaluated, while deep features extracted using pre-trained VGG and ResNet models are classified by Random Forest, K-Nearest Neighbors, and a classifier-level ensemble. The optimal Unsharp--ResNet--Random Forest configuration achieved 80.0\% accuracy with an F1-score of 87.0\%. ADASYN applied in the deep feature space improved minority-class sensitivity, while Grad-CAM and Grad-CAM++ enhanced model interpretability. The proposed framework provides an effective and transparent solution for practical field-level brinjal disease detection and precision agriculture.
Agriculture is a key sector in developing economies, but crop diseases significantly impact productivity, food security, and farmers’ livelihoods. Early detection is crucial to minimize losses, yet traditional methods are slow, error-prone, and depend heavily on human expertise. Recent advancements in Artificial Intelligence (AI), particularly machine learning (ML) and deep learning (DL), have enabled more efficient automated crop disease detection. This study reviews pre-2018 AI-based approaches, focusing on techniques such as image processing, feature extraction, and classification methods. It highlights models like Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and hybrid systems combining traditional and modern techniques. The proposed approach includes preprocessing, segmentation, color transformation, and extraction of texture, color, and shape features, followed by supervised learning for classification. AI systems can detect subtle disease symptoms early, achieving over 90% accuracy under controlled conditions. Integration with mobile and IoT technologies enables real-time monitoring and decision support for farmers. However, challenges such as limited datasets, environmental variability, and computational constraints remain. Future work should focus on developing scalable, robust, and field-deployable solutions for diverse agricultural conditions.
A scalable image-based framework for automatic rice leaf disease detection and monitoring using deep learning and cloud-enabled analytics and enables deployment on smartphones, drones, and edge devices for smart agriculture applications.