[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-744":3,"related-744":61},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":55,"slug":58,"view_count":59,"doi":8,"paper":8,"created_at":60},744,"Swin Transformer+ConvNeXt双流融合的水稻叶部病害分类","https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528","提出全局-局部双流融合网络,叠加注意力机制+Grad-CAM可视化+大语言模型农艺建议;Dataset-1准确率99.55%、五折交叉验证98.91%±0.47,跨数据集九类公共病害准确率达90.80%。",null,"## 1. Introduction\n\nRice (Oryza sativa) is one of the most important staple crops, providing a primary food source for more than 50% of the global population. However, rice production is continuously affected by various foliar diseases, including Neck Blast, Leaf Scald, Sheath Blight, Narrow Brown Leaf Spot, Leaf Blast, Rice Hispa, Brown Spot, and Bacterial Leaf Blight. These diseases significantly reduce crop productivity and quality, creating serious challenges for global food security, particularly in developing countries where agriculture plays a major role in the economy. Furthermore, favorable environmental conditions can accelerate disease propagation, emphasizing the importance of accurate and timely disease diagnosis for effective crop management [[1](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B1-computers-15-00528),[2](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B2-computers-15-00528),[3](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B3-computers-15-00528)].\n\nTraditionally, rice disease identification has relied on expert visual inspection and laboratory-based analysis [[4](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B4-computers-15-00528)]. Although these approaches can provide reliable diagnosis, they are often time-consuming, labor-intensive, and difficult to access for small-scale farmers in remote agricultural regions. Inaccurate or delayed disease identification may lead to inappropriate pesticide application, increasing production costs and causing potential environmental impacts [[5](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B5-computers-15-00528)]. Therefore, automated and accessible disease recognition systems have become increasingly important for supporting efficient agricultural practices.\n\nIn recent years, deep learning (DL)-based approaches, particularly Convolutional Neural Networks (CNNs), have demonstrated significant potential for plant disease classification using leaf images. CNN-based methods effectively extract local spatial patterns, including color variations, textures, and lesion characteristics, enabling high classification performance under controlled experimental conditions [[6](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B6-computers-15-00528)]. However, CNN-based models may face challenges in capturing long-range dependencies and global contextual relationships due to their inherently local receptive field operations [[2](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B2-computers-15-00528)]. These limitations can affect their ability to fully represent complex disease patterns that involve both localized symptoms and broader leaf-level characteristics.\n\nTo address the limitations associated with convolution-based feature extraction, Vision Transformers (ViTs) have recently gained attention due to their ability to model global relationships through self-attention mechanisms. Unlike CNNs, transformer-based architectures represent images as sequences of patches, enabling comprehensive analysis of spatial dependencies across the entire image [[7](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B7-computers-15-00528)]. Consequently, transformer-based methods have demonstrated improved robustness and competitive performance in various plant disease recognition tasks [[8](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B8-computers-15-00528)]. Furthermore, a deep learning-based ROA-DM framework has been proposed for automated rice leaf disease classification, integrating a Deep Autoencoder (DAE), Deep Max-out Network (DMN), and Remora Optimization Algorithm (ROA) [[9](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B9-computers-15-00528)]. However, despite their high predictive performance, many existing DL-based agricultural diagnosis systems still face challenges related to model interpretability. These models often operate as black-box systems, providing predictions without sufficient explanations regarding disease-related visual evidence. Such limitations reduce user confidence and hinder practical adoption in agricultural applications. Therefore, Explainable Artificial Intelligence (XAI) techniques, such as Gradient-weighted Class Activation Mapping (Grad-CAM) and attention visualization, have become increasingly important for identifying disease-relevant regions and improving model transparency [[10](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B10-computers-15-00528)].\n\nBeyond accurate and interpretable disease recognition, practical agricultural applications require intelligent decision-support capabilities that can provide understandable and actionable recommendations to farmers. Recently, Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding and knowledge-based reasoning. The integration of LLMs with disease recognition systems provides opportunities to generate disease-specific management recommendations, preventive strategies, and treatment guidance, thereby bridging the gap between automated diagnosis and agricultural decision-making [[11](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B11-computers-15-00528),[12](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B12-computers-15-00528)]. Nevertheless, many existing studies remain primarily focused on offline model evaluation, while challenges related to real-time deployment, user interaction, interpretability, and practical usability remain insufficiently addressed. Therefore, an integrated framework combining accurate classification, explainable prediction, and intelligent advisory support is highly desirable for real-world smart farming applications [[13](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B13-computers-15-00528)].\n\nTo address these challenges, this study proposes a dual-backbone complementary representation learning framework for accurate and interpretable rice leaf disease diagnosis. The proposed framework integrates Swin Transformer and ConvNeXt [[14](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B14-computers-15-00528)] architectures to exploit complementary visual representations. Specifically, Swin Transformer is employed to capture global contextual dependencies and long-range spatial relationships, whereas ConvNeXt is utilized to extract fine-grained local texture features associated with disease symptoms. An attention-based feature fusion module is further applied to adaptively combine these heterogeneous feature representations and enhance discriminative disease feature learning. Additionally, Grad-CAM is incorporated to provide visual explanations by highlighting disease-relevant regions contributing to model predictions. Furthermore, a web-based LLM advisory module is introduced as a post-diagnosis decision-support component to generate disease-specific management recommendations based on the predicted disease category. The LLM module is used exclusively for advisory purposes and does not participate in feature extraction, classification, or performance evaluation.\n\nThe main contributions of this study are summarized as follows:\n\n*   A dual-backbone complementary representation learning framework is proposed by integrating Swin Transformer and ConvNeXt to capture global contextual information and local disease-specific texture characteristics.\n\n*   An attention-based adaptive feature fusion mechanism used to combine complementary feature representations and improve discriminative disease classification.\n\n*   A comprehensive evaluation framework is established using multiple state-of-the-art baseline models, cross-validation, and cross-dataset testing for robustness and generalization capability.\n\n*   An explainable disease diagnosis mechanism based on Grad-CAM is incorporated to improve model transparency and identify disease-relevant image regions.\n\n*   A practical web-based decision-support extension with an LLM-based advisory module is introduced to provide disease-specific agricultural recommendations after diagnosis.\n\nThe remainder of this paper is organized as follows. [Section 2](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#sec2-computers-15-00528) presents the related work, including recent advances in rice leaf disease diagnosis, transformer-based architectures, explainable artificial intelligence, and intelligent agricultural decision-support systems. [Section 3](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#sec3-computers-15-00528) describes the proposed methodology, including dataset preparation, preprocessing, dual-backbone Swin Transformer-ConvNeXt architecture, attention-based feature fusion, Grad-CAM-based explainability, and the post-diagnosis LLM advisory module. [Section 4](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#sec4-computers-15-00528) presents experimental results and detailed analysis, including comparisons with baseline models, ablation studies, cross-validation results, cross-dataset evaluation, confusion matrix analysis, ROC-AUC analysis, and explainability assessment. [Section 5](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#sec5-computers-15-00528) discusses the web-based deployment framework and its applicability for smart agricultural practices. Finally, [Section 6](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#sec6-computers-15-00528) concludes the paper and highlights future research directions.\n\n## 2. Related Work\n\nThe growing need for intelligent and scalable agricultural solutions has made automated rice leaf disease detection an active research topic. In recent years, deep learning-based solutions have greatly enhanced the precision and effectiveness of plant disease diagnosis. Nevertheless, the current approaches differ in their capacity to reflect complicated features, be interpretable, and be applicable in the real world. Convolutional Neural Networks (CNNs) have been widely applied to rice leaf disease classification because they are powerful at extracting local spatial features like texture, color, and lesion patterns. For example, Nguyen et al. [[15](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B15-computers-15-00528)] introduced an LCSNet–FedPerGC framework for rice leaf disease classification, which is a combination of a lightweight CNN and an optimized federated learning strategy. The framework was shown to be highly accurate for classification, efficient for inference and robust with respect to both IID and non-IID data distributions. A similar approach was used to introduce a Concurrency Attention-Based CNN for rice leaf disease recognition under different lighting conditions, which uses a multiple-parallel attention mechanism to capture discriminative features and ignore background interference, with a classification accuracy of 96.2% [[16](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B16-computers-15-00528)]. Moreover, Twin-CNN structures [[17](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B17-computers-15-00528)] have shown enhanced performance through the application of dual-stream feature extraction. Despite the promising results obtained using CNN-based models, they mainly use local receptive fields, which restricts their capacity to extract long-range dependencies and global contextual information. As a result, they can perform poorly in real-life situations with complex backgrounds, occlusions, and different illumination. Transformer-based models have been suggested to address the shortcomings of CNNs in detecting plant diseases. Vision Transformers (ViTs) are self-attention models that are used to capture global relations between image patches, enhancing contextual knowledge. As shown by Singha Roy and Kukreja [[18](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B18-computers-15-00528)], ViT-based models are more robust when it comes to rice leaf disease classification. Likewise, AISOA-SSformer [[19](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B19-computers-15-00528)] uses a transformer-based segmentation framework with sparse attention to boost disease localization. In addition, Swin Transformer-based approaches [[20](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B20-computers-15-00528)] have shown strong performance by incorporating hierarchical feature representation and shifted window attention mechanisms. Explainable transformer models [[21](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B21-computers-15-00528)] further improve transparency by incorporating attention visualization. Nevertheless, the benefits of transformer-based models are not without drawbacks as they typically need massive datasets and high-performance computing, and most current implementations cannot be explained or deployed. More recent studies have concentrated on hybrid architectures that combine CNN and transformer models to utilize both local and global feature representations. As an example, GRG-ViT [[22](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B22-computers-15-00528)] combines generative adversarial networks (GANs) with Vision Transformers to deal with the issue of class imbalance and enhance generalization. Likewise, hybrid CNN–transformer models [[23](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B23-computers-15-00528)] exhibit better performance due to the integration of convolutional feature extraction and global attention mechanisms. In addition, ConvNeXt-based architectures [[24](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B24-computers-15-00528)] are also suggested as CNN backbone modernizations that enable a transition between CNNs and transformers. Hybrid models based on multiple AI models that are green and emphasize computational efficiency and sustainability are also highlighted as green AI-based hybrid models [[25](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B25-computers-15-00528)]. Nevertheless, A considerable number of hybrid models do not include integrated explainability analysis and do not encompass the use of better classification performance, even though they are more effective than their counterparts. Explainable Artificial Intelligence (XAI) has become a key element of enhancing trust and transparency in AI-based agricultural systems. Grad-CAM and other techniques have been extensively applied to identify disease-related areas on leaf images [[26](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B26-computers-15-00528)]. Transformer-based explainability techniques [[21](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B21-computers-15-00528)] further increase interpretability through the use of attention maps. Despite the fact that these methods offer great insight into how models make decisions, they are not usually incorporated into a holistic decision-support system but are used as modules on their own. The combination of Large Language Models (LLMs) with vision-based systems is a recent development that has introduced new opportunities in intelligent agriculture. Advisory frameworks assisted by an LLM [[27](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B27-computers-15-00528)] can be used to produce disease-specific recommendations, such as treatment plans and prevention. Multimodal vision-language models [[28](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B28-computers-15-00528),[29](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B29-computers-15-00528)] also add more capabilities to the system by integrating image comprehension with natural language reasoning. Also, research emphasizes the need to go beyond dataset-based assessment to real-world usage [[30](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B30-computers-15-00528)]. Practical AI solutions for farmers are feasible as illustrated by web-based deployment frameworks and real-time mobile systems [[31](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B31-computers-15-00528)] and [[32](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B32-computers-15-00528)], respectively. Nevertheless, the current LLM implementations tend to be disconnected with the model of explainable visions, which prevents them from connecting predictions with practical insights. Although recent progress has been made, a number of problems still exist. First, the existing approaches focus mainly on classification performance and do not fully consider the robustness of the system in its real-world applications involving changes in lighting conditions, background complexity and other environmental factors. Second, many hybrid CNN–Transformer models have been proposed to improve feature representation, but most do not yet fully leverage the complementary characteristics of global information and fine-grained local information related to disease textures via adaptive feature learning. Third, there has been little time spent on incorporating explainable AI approaches, which could boost the openness and credibility of disease prognosis. Finally, although there are many research papers that look at automatic disease detection, there are limited studies have explored integrating diagnosis models with farmer-oriented decision-support systems. In view of this, a dual-backbone complementary representation learning network is proposed to tackle the above problems, which involves using both Swin Transformer and ConvNeXt with an attention-based feature fusion module to provide a robust and interpretable rice leaf disease diagnosis. A list of suggested management recommendations is also included using an LLM-based advisory system, which is further augmented with a web-enabled system, and Grad-CAM to offer visual explanations.\n\n[Table 1](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#table_body_display_computers-15-00528-t001) shows the quantitative comparison of various methods used for detecting diseases on rice leaves. Some of the important characteristics include model structure, explainability, advisibility, deployment, robustness, and scalability.\n\n**Table 1.** Comparison of recent rice leaf disease detection methods.\n\nAs summarized in [Table 1](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#table_body_display_computers-15-00528-t001), most of the current techniques are mainly employed for classification accuracy and\u002For feature extraction, lacking a holistic and verifiable diagnostic framework. CNN-based approaches [[15](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B15-computers-15-00528),[16](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B16-computers-15-00528)] well model local visual patterns with limited capability of modeling long-range contextual dependencies. Transformer-based methods [[19](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B19-computers-15-00528)] have been employed to enhance the modeling of global context, but they generally have limited interpretability and applicability. Current hybrid methods [[22](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B22-computers-15-00528),[23](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B23-computers-15-00528),[25](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F528#B25-computers-15-00528)] blend CNNs and Transformers; however, they most commonly integrate features using static or identity mappings and fail to substantially leverage the complementary feature representations using adaptive attention mechanisms. The proposed model, on the contrary, adopts the dual-backbone complementary representation learning scheme and attention-based feature fusion for simultaneous learning of global contextual and local texture representation and generalization of disease characterization. Additionally, Grad-CAM makes the model more interpretable and the LLM advisory module, accessible via the web, is used only as a post-diagnosis decision-support component (presenting management suggestions to the farmer rather than involvement in disease classification). The entire system is implemented on the Hugging Face platform, facilitating fast and on-the-go inference and deployment in the field of smart agriculture.\n\n## 3. Dataset Description\n\n### 3.1. Dataset-1\n\nThis study utilizes a publicly available rice leaf disease image dataset collected from real-world agricultural field environments. The dataset was obtained from the Kaggle platform and contains high-resolution images representing different healthy and diseased rice leaf conditions. Since the images were collected in practical field environments, the dataset includes considerable variations in illumination, background complexity, leaf orient","MDPI Computers 15(8):528 2026-08-14","2026-08-14T00:00:00Z","论文",10,false,63,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},12,18,16,5,1,"论文提出Swin Transformer与ConvNeXt双流融合模型用于水稻叶部病害分类，方法新颖，数据详实，但影响范围限于细分领域，时效性一般。",[24],{"name":10,"url":6},[26,27,28,29,30],"多模态","智慧种植","水稻病害","Swin-ConvNeXt","注意力",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,50],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":49},9,true,[34],[],[28],{"code":51,"score":21,"supported":14,"title":52,"abstract":53,"tags":54},"I2",[],[],[26],[56,57],"智慧种植 水稻病害 多模态 注意力","智慧种植 水稻病害","智慧种植水稻病害多模态注意力-744",2,"2026-08-19T23:57:32.325006Z",{"total":62,"page":21,"page_size":62,"items":63},6,[64,153,209,264,310,359],{"id":65,"title":66,"url":67,"summary":68,"summary_zh":69,"content":8,"source_name":70,"source_url":67,"published_at":71,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":72,"score_detail":73,"sources":77,"tags":79,"primary_topic":84,"secondary_topics":85,"classification":96,"search_phrases":123,"slug":126,"view_count":59,"doi":127,"paper":128,"created_at":152},2646,"IoT-RiceMobileNet: An improved lightweight MobileNetV2 Model for real-time multi-class rice disease detection using IoT","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0356383","Early and real-time detection of rice leaf diseases (RLD) poses a significant challenge for farmers, especially in rural regions with limited access to advanced technology. Conventional deep learning models often require substantial computational resources, rendering them impractical for deployment on mobile or edge devices commonly used in agricultural environments. Although models such as ResNet50, VGG16, and InceptionV3 can achieve high accuracy, they are computationally expensive and may be less suitable for real-time deployment in smart agricultural systems. Furthermore, many existing studies rely on limited datasets, which can restrict model generalizability under diverse real-world conditions. Although transfer learning can improve classification performance, developing lightweight models suitable for real-time IoT deployment remains challenging. To address these limitations, we curated a hybrid dataset of 7,092 rice leaf images by combining self-collected and Kaggle samples and proposed IoT-RiceMobileNet, a lightweight improved MobileNetV2-based model that can be effectively integrated into our developed IoT system. The proposed model outperformed transfer learning and deep learning baseline models, achieving 99.19% test accuracy and 99.20% precision while maintaining low computational complexity. We further confirmed model stability using stratified 5-fold and 10-fold cross-validation on both the constructed RLD and multi-source datasets, achieving mean accuracies of 98.04% and 98.13% on the constructed RLD dataset and 98.11% and 98.58% on the multi-source dataset, respectively. Moreover, our models compact size and 85.17 FPS inference speed support real-time deployment. Finally, the model was integrated into an IoT-enabled mobile, web, and cloud-based inference framework, demonstrating its practical potential for scalable rice disease detection in smart agriculture.","水稻叶片病害（RLD）的早期和实时检测对农民而言是一项重大挑战，尤其是在难以获取先进技术的农村地区。传统的深度学习模型通常需要大量计算资源，使其难以在实际农业环境中常用的移动或边缘设备上部署。尽管ResNet50、VGG16和InceptionV3等模型可以达到较高精度，但其计算成本高昂，可能不太适合在智慧农业系统中实时部署。此外，许多现有研究依赖有限的数据集，这可能限制模型在多样化真实条件下的泛化能力。尽管迁移学习可以提升分类性能，但开发适合实时物联网部署的轻量级模型仍具有挑战性。为解决这些局限，我们通过整合自采集样本和Kaggle样本，构建了一个包含7092张水稻叶片图像的混合数据集，并提出了IoT-RiceMobileNet，这是一种基于MobileNetV2改进的轻量级模型，可有效集成到我们开发的物联网系统中。所提出的模型优于迁移学习和深度学习基线模型，在保持低计算复杂度的同时，实现了99.19%的测试准确率和99.20%的精确率。我们进一步通过在构建的RLD数据集和多源数据集上采用分层5折和10折交叉验证确认了模型稳定性，在构建的RLD数据集上分别达到98.04%和98.13%的平均准确率，在多源数据集上分别达到98.11%和98.58%的平均准确率。此外，该模型紧凑的规模和85.17 FPS的推理速度支持实时部署。最后，该模型被集成到一个支持物联网的移动端、网页端和云端推理框架中，展示了其在智慧农业中可扩展水稻病害检测的实际潜力。","PLoS ONE","2026-09-15T00:00:00Z",80,{"impact":18,"substance":74,"depth":18,"authority":75,"freshness":45,"relevant":21,"comment":76},22,13,"提出轻量级MobileNetV2水稻病害检测模型并集成物联网推理框架，实测精度与推理速度俱佳，方法新颖、数据规模可观，对边缘端智慧农业落地有参考价值。",[78],{"name":70,"url":67},[80,81,82,28,83],"智慧农业","农业人工智能","物联网","轻量化模型",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[86,92],{"code":87,"slug":88,"name":89,"path":90,"parent_code":91},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"code":51,"slug":93,"name":94,"path":95,"parent_code":91},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":39,"locked":14,"version":40,"method":41,"evidence":97},{"candidates":98,"agricultural_evidence":46},[99,104,109,116],{"code":32,"score":100,"supported":46,"title":101,"abstract":102,"tags":103},15,[33],[34,33],[28],{"code":87,"score":100,"supported":46,"title":105,"abstract":107,"tags":108},[106],"IoT",[82,106],[82],{"code":51,"score":110,"supported":46,"title":111,"abstract":112,"tags":115},8,[],[113,114],"深度学习","deep learning",[81,83],{"code":117,"score":118,"supported":46,"title":119,"abstract":120,"tags":122},"I1",7,[],[80,121],"smart agriculture",[80],[124,125],"农业人工智能 轻量化模型 智慧农业 水稻病害","农业人工智能 轻量化模型","农业人工智能轻量化模型智慧农业水稻病害-2646","10.1371\u002Fjournal.pone.0356383",{"doi":127,"openalex_id":129,"authors":130,"venue":70,"cited_by_count":144,"oa_url":67,"card":145,"direction":149,"ingested_from":151},"W7213328321",[131,133,135,137,139,141],{"name":132,"orcid":8},"Khawja Imran Masud",{"name":134,"orcid":8},"Md Yasin Zihad",{"name":136,"orcid":8},"Mehedi Hasan Shuvo",{"name":138,"orcid":8},"Mst Raonik Jannat",{"name":140,"orcid":8},"Jia Uddin",{"name":142,"orcid":143},"Sahara Ali","https:\u002F\u002Forcid.org\u002F0000-0002-8578-948X",0,{"tldr":146,"method":147,"finding":148,"direction":149,"opportunity":150},"提出轻量级IoT-RiceMobileNet模型，实现水稻病害实时多类检测。","改进MobileNetV2，融合自采与Kaggle共7092张图像，IoT端部署","测试准确率99.19%，推理速度85.17 FPS，适合边缘设备实时检测。","智慧农业 \u002F 农业物联网","可探索多作物病害泛化、田间复杂光照下轻量模型鲁棒性及边缘联邦学习。","openalex","2026-09-16T23:30:12.197386Z",{"id":154,"title":155,"url":156,"summary":157,"summary_zh":8,"content":8,"source_name":158,"source_url":8,"published_at":159,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":160,"score_detail":161,"sources":165,"tags":167,"primary_topic":169,"secondary_topics":170,"classification":172,"search_phrases":195,"slug":198,"view_count":21,"doi":8,"paper":199,"created_at":208},2491,"DASO-RiceNet: 细粒度水稻病害与损伤分类的双注意力序列优化网络(准确率 96.5%)","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F18\u002F1945","台湾朝阳科技大学 Yung-Fa Huang 等 2026 年 9 月 9 日在《Agriculture》发表(专刊'Applying Artificial Intelligence to Sustainable Crop Protection: Managing Pests and Diseases')。提出 DASO-RiceNet(Dual-Attention Semantic Optimization Network)深度学习框架,采用多阶段残差骨干进行特征提取,序列双注意力模块融合通道与空间注意力以强调诊断相关特征并抑制背景信息。在 10 类水稻病害与损伤数据集上,DASO-RiceNet 准确率 0.965、宏精确率 0.963、宏召回率 0.966、宏 F1 0.964,优于所对比的 CNN 与 Transformer 基线模型。Grad-CAM 和 LIME 提供了模型预测的定性洞察。","MDPI Agriculture 2026-09-09","2026-09-08T16:00:00Z",75,{"impact":19,"substance":162,"depth":163,"authority":75,"freshness":110,"relevant":21,"comment":164},21,17,"细粒度水稻病害识别新框架，指标扎实且具可解释性，对作物病虫害智能诊断有参考价值。",[166],{"name":158,"url":156},[80,81,168,113,28],"可解释AI",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[171],{"code":51,"slug":93,"name":94,"path":95,"parent_code":91},{"status":39,"locked":14,"version":40,"method":41,"evidence":173},{"candidates":174,"agricultural_evidence":46},[175,179,184,191],{"code":32,"score":17,"supported":46,"title":176,"abstract":177,"tags":178},[34],[34],[28],{"code":51,"score":45,"supported":46,"title":180,"abstract":181,"tags":183},[],[113,182],"artificial intelligence",[81,168,113],{"code":185,"score":186,"supported":46,"title":187,"abstract":188,"tags":190},"F4",3,[],[189],"crop protection",[],{"code":117,"score":21,"supported":14,"title":192,"abstract":193,"tags":194},[],[],[80],[196,197],"农业人工智能 智慧农业 水稻病害 深度学习","农业人工智能 智慧农业","农业人工智能智慧农业水稻病害深度学习-2491",{"doi":8,"openalex_id":8,"authors":200,"venue":8,"cited_by_count":144,"oa_url":8,"card":201,"direction":205,"ingested_from":207},[],{"tldr":202,"method":203,"finding":204,"direction":205,"opportunity":206},"提出双注意力序列优化网络DASO-RiceNet，实现10类水稻病害与损伤的高精度分类。","多阶段残差骨干+序列双注意力（通道与空间），在10类水稻病害数据集上训练评估。","准确率96.5%、宏F1 0.964，优于CNN与Transformer基线，Grad-CAM\u002FLI","农业人工智能与决策模型","可探索轻量化部署与田间复杂背景下的跨域泛化，并结合多模态数据提升早期病害预警能力。","agent","2026-09-15T00:04:27.118669Z",{"id":210,"title":211,"url":212,"summary":213,"summary_zh":214,"content":8,"source_name":215,"source_url":212,"published_at":216,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":217,"score_detail":218,"sources":221,"tags":223,"primary_topic":225,"secondary_topics":226,"classification":228,"search_phrases":245,"slug":248,"view_count":59,"doi":249,"paper":250,"created_at":263},2068,"Leakage-Proof Reliability-Aware Deep Learning Framework for Rice Leaf Disease Diagnosis","https:\u002F\u002Fdoi.org\u002F10.14445\u002F22315381\u002Fijett-v74i8p113","Timely and accurate diagnosis of rice leaf diseases is significant in order to achieve reduced loss and precision agriculture management. In this paper, a leakage-aware and reliability-oriented deep learning framework is proposed to classify rice leaf diseases based on the Mendeley rice leaf disease dataset. In order to avoid the problem of similar and duplicate images in the training and validation set, the framework incorporates perceptual hashing and stratified group K-fold cross-validation. The evaluation measures the accuracy of the in-domain classification, robustness to controlled image corruption, evaluation of the Expected Calibration Error and temperature scaling methods, selective prediction using risk-coverage analysis, external-dataset validation, and computational efficiency profiling. The in-domain evaluation resulted in an almost perfect score. The accuracy achieved by the cross-validation method applied to the folds evaluated showed high values, while the external UCI rice leaf dataset presented significantly lower values, with an accuracy of 54.17% and macro-F1 of 48.98%. This contrast suggests that high in-domain accuracy should be considered with caution if there is a domain shift. In addition, efficiency and reliability analysis indicated that mid-scale convolutional models, especially ResNet-based models, achieved a better trade-off between predictive performance and computational cost compared to larger high-capacity models. The suggested assessment scheme in the proposed framework then focuses not only on accuracy but also on deployment-oriented evaluation in the context of rice leaf disease diagnosis with the help of AI.","及时、准确地诊断水稻叶片病害对于减少损失和实现精准农业管理具有重要意义。本文基于Mendeley水稻叶片病害数据集，提出了一种感知泄漏且面向可靠性的深度学习框架，用于水稻叶片病害分类。为避免训练集与验证集中出现相似和重复图像的问题，该框架引入了感知哈希（perceptual hashing）和分层分组K折交叉验证。评估内容包括域内分类准确率、对受控图像损坏的鲁棒性、期望校准误差（Expected Calibration Error）与温度缩放方法的评估、基于风险-覆盖率分析的选择性预测、外部数据集验证以及计算效率分析。域内评估取得了近乎完美的分数。应用于各折评估的交叉验证方法所达到的准确率较高，而外部UCI水稻叶片数据集则呈现出显著较低的值，准确率为54.17%，宏F1为48.98%。这一对比表明，若存在域偏移，高域内准确率应谨慎看待。此外，效率与可靠性分析表明，中等规模的卷积模型，尤其是基于ResNet的模型，相较于更大的高容量模型，在预测性能与计算成本之间实现了更好的权衡。因此，所提框架中的建议评估方案不仅关注准确率，还关注在人工智能辅助水稻叶片病害诊断背景下面向部署的评估。","International Journal of Engineering Trends and Technology","2026-09-09T00:00:00Z",70,{"impact":17,"substance":219,"depth":163,"authority":17,"freshness":45,"relevant":21,"comment":220},20,"提出防数据泄漏与可靠性评估的水稻叶病诊断框架，外部数据集准确率骤降至54.17%，对农业AI落地评估有实质参考价值。",[222],{"name":215,"url":212},[80,81,113,28,224],"模型可靠性",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[227],{"code":51,"slug":93,"name":94,"path":95,"parent_code":91},{"status":39,"locked":14,"version":40,"method":41,"evidence":229},{"candidates":230,"agricultural_evidence":46},[231,235,239],{"code":51,"score":19,"supported":46,"title":232,"abstract":233,"tags":234},[114],[113,114],[81,113],{"code":32,"score":100,"supported":46,"title":236,"abstract":237,"tags":238},[33],[34,33],[28],{"code":117,"score":118,"supported":46,"title":240,"abstract":241,"tags":244},[],[242,243],"精准农业","precision agriculture",[80],[246,247],"农业人工智能 模型可靠性 智慧农业 水稻病害","农业人工智能 模型可靠性","农业人工智能模型可靠性智慧农业水稻病害-2068","10.14445\u002F22315381\u002Fijett-v74i8p113",{"doi":249,"openalex_id":251,"authors":252,"venue":215,"cited_by_count":144,"oa_url":257,"card":258,"direction":205,"ingested_from":151},"W7212016871",[253,255],{"name":254,"orcid":8},"Sujeet Kumar",{"name":256,"orcid":8},"Prajeet Sharma","https:\u002F\u002Fijettjournal.org\u002FVolume-74\u002FIssue-8\u002FIJETT-V74I8P113.pdf",{"tldr":259,"method":260,"finding":261,"direction":205,"opportunity":262},"提出防泄漏、可靠性导向的水稻叶病诊断深度学习框架，并系统评估其泛化与部署表现。","感知哈希与分层组K折交叉验证，结合校准、选择性预测及外部数据集验证。","域内准确率近完美，但外部UCI数据集仅54.17%，域偏移下高精度需谨慎。","可研究跨域自适应与轻量模型校准，提升叶病诊断在真实田间场景的泛化可靠性。","2026-09-10T23:30:43.799587Z",{"id":265,"title":266,"url":267,"summary":268,"summary_zh":8,"content":269,"source_name":270,"source_url":8,"published_at":271,"category":272,"cover_url":8,"hotness":13,"is_selected":14,"score":273,"score_detail":274,"sources":277,"tags":279,"primary_topic":285,"secondary_topics":286,"classification":287,"search_phrases":305,"slug":308,"view_count":144,"doi":8,"paper":8,"created_at":309},5450,"福建农科院水稻所「禾两优631」再生稻示范 两季亩产 1364.52 公斤","https:\u002F\u002Fwww.faas.cn\u002Fcms\u002Fhtml\u002Ffjsnykxy\u002F2026-10-19\u002F849994658.html","10 月 15 日，福建省农业科学院水稻所在尤溪县西城镇麻洋村举行「禾两优631」作再生稻高产栽培示范验收会，中国科学院韩斌、钱前院士等到场测产。经测产该品种再生季亩产 484.72 公斤，加上 8 月头季亩产 879.8 公斤，两季合计亩产 1364.52 公斤，是麻洋村基地连续第 26 年开展再生稻高产示范的新进展。「禾两优631」为两系杂交中稻，2025 年通过福建省中稻审定（闽审稻 20251009），抗白叶枯病、中抗稻瘟病、再生力强，2026 年福建省农业主导品种。","10月15日，我院水稻所在尤溪县西城镇麻洋村举行新品种“禾两优631”作再生稻高产栽培示范验收会。中国科学院韩斌院士、钱前院士以及南京农业大学、中国农业科学院作物科学研究所、上海市农业科学院、福建省农业农村厅、福建省农学会和三明市农业农村局的专家莅临现场测产。谢华安院士，我院党委委员、副院长魏辉研究员到会指导，院科研处处长赵健研究员主持验收会。\n\n经测产，“禾两优631”再生季亩产达484.72公斤，加上今年8月头季亩产879.8公斤，两季合计亩产1364.52公斤。水稻所又一新品种实现再生稻栽培高产，也是该基地连续第26年开展再生稻高产栽培示范取得的新进展。验收专家们表示，“禾两优631”头季与再生季表现均衡，再生季484.72公斤的产量表明该品种具有较强再生力和推广潜力，也验证了配套栽培技术在大面积示范中的稳定性。两季合计1364.52公斤，对提升稻田综合效益、稳定粮食生产、促进农民增收具有重要意义。\n\n尤溪县是全国高产再生稻发祥地。自2000年以来，我院水稻所的科研人员风雨无阻、年复一年，在同一片土地上持续开展再生稻品种试验、高产栽培和示范推广。截至目前，麻洋村百亩示范基地已示范种植15个强再生力杂交稻品种。麻洋村百亩示范基地已成为福建再生稻品种与技术的重要展示窗口。谢华安院士团队经数十年持续攻关，不仅选育出一批高产、抗逆、优质、广适的强再生力水稻新品种；还系统构建了水稻再生力评价体系，创建了以“双调双控”为核心的再生稻高效生产关键技术体系和省工节本的机械化生产技术体系，相关科研成果获得福建省科技进步一等奖2项。这既是26年厚积薄发的集中体现，也是对持之以恒开展基础研究和应用推广的最好肯定。\n\n“禾两优631”是我院水稻所主持选育的两系杂交中稻品种，2025年通过福建省中稻审定（闽审稻20251009），进入国家长江中下游中籼迟熟组区域试验（第2年）。 “禾两优631”田间表现产量高（LAX1\u002FPTB1\u002FNOG1）、抗白叶枯病（Xa23）、中抗稻瘟病（Pi1\u002FPi2\u002FPikh）、再生力强（psr1）、适应性强（OsLG3）、抗倒伏，是一个综合性状优良的杂交稻品种。“禾两优631”被评为2024年全国种子双交会水稻地展评价优良品种、2025年福建省优质稻田间展示综合表现优异十大品种、2026年福建省农业主导品种。“禾两优631”的育成和应用，将为福建省乃至长江中下游地区的粮食生产作出更大贡献。\n\n站在新的历史起点上，水稻所将继续保持“咬定青山不放松”的战略定力，以更高标准、更实举措推进再生稻科研与产业高质量发展，在融合分子育种等现代技术手段强化育种攻关、推进农机农艺融合、切实提升生产效率和深化示范推广与产业带动等方面，加快技术成果在全省乃至南方稻区的推广应用，为南方稻区再生稻产业发展提供可借鉴的样板，为保障国家粮食安全、促进农业增效和农民增收作出新的更大贡献。（文\u002F水稻所 朱永生，图\u002F水稻所 蔡秋华）","福建省农业科学院 2026-10-19","2026-10-19T00:00:00Z","报道",82,{"impact":74,"substance":74,"depth":19,"authority":275,"freshness":110,"relevant":21,"comment":276},14,"省级科研机构发布的新品种实收测产数据具体，两季亩产1364.52公斤且连续26年示范，具行业参考价值，值得进入每日精选。",[278],{"name":270,"url":267},[280,281,282,283,284],"种业振兴","粮食安全","再生稻","水稻育种","高产示范",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":288},{"candidates":289,"agricultural_evidence":46},[290,295,300],{"code":32,"score":291,"supported":46,"title":292,"abstract":293,"tags":294},24,[34,282],[34,282],[282,283],{"code":296,"score":59,"supported":14,"title":297,"abstract":298,"tags":299},"A8",[],[],[281,284],{"code":301,"score":21,"supported":14,"title":302,"abstract":303,"tags":304},"F1",[],[],[280],[306,307],"禾两优631 再生稻","福建农科院 水稻所 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日上午，水稻良种和高效肥展示观摩会在淮南市潘集区贺疃镇「绿色吨半粮」示范基地举行，来自中国农业大学、南京农业大学、安徽省农业科学院、安徽农业大学的专家教授以及种粮大户、基层农技人员参加。示范田内多个水稻新品种分区种植，同步设置高效肥与常规施肥对照试验田块，通过机收和人员测产直观展示良种搭配高效肥料的增产效果，为做好新一年店集贡米品种选育与宣传推广提供技术依据，推动水稻单产提升行动。","为加快优良水稻品种、高效新型肥料示范推广，推动良种良法配套落地，助力粮食稳产增产，10月14日上午，水稻良种和高效肥展示观摩会在贺疃镇“绿色吨半粮”示范基地举行。来自中国农业大学、南京农业大学、安徽省农业科学院、安徽农业大学的专家教授以及种粮大户、基层农技人员齐聚基地，现场观摩对比，开展实收测产。\n\n在示范田内，多个水稻新品种分区种植，同步设置高效肥与常规施肥对照试验田块。观摩会现场，通过查看不同品种的长势、抗倒伏、抗病性，对比施用高效肥和常规肥料水稻的株型、穗粒、结实情况，现场讲解品种特性、科学施肥、田间管护等关键技术要点，为种粮大户答疑解惑，做好新一年的店集贡米的品种选育、宣传推广。现场通过机收和人员测产，直观展示了优良品种搭配高效肥料的增产效果，用真实数据验证良种与高效肥相结合的增产提质、节肥增效优势，对推进水稻单产提升行动具有积极作用。\n\n潘集区农技推广中心主任杨中保表示：“近年来，我们立足本地农业生产实际，聚焦粮食安全底线、农业增效目标、农民增收核心，扎实推进农作物良种筛选推广、配套栽培良法落地、新型肥料示范应用等一系列重点工作，通过建示范、抓培训、强指导、树样板，良种良法配套、科学施肥增效的农业生产新模式全面落地，让农作物抗灾能力、单产水平、农产品品质稳步提升，农业生产综合效益持续提高，农业生产取得了阶段性成效。”\n\n通过现场观摩不同品种的水稻生长特性，选用适配本地气候土壤的水稻良种，配套应用高效肥料，能够提升肥料利用率，对于减少化肥投入，改善稻米品质，推动水稻绿色高产，壮大潘集水稻产业的有效路径。此次观摩测产活动，搭建起科研院所、农技部门与种植主体交流对接的平台，打通农业科技成果转化 “最后一公里”。\n\n潘集区农技推广中心主任杨中保表示：“下一步，我们农技部门要锚定农业高质量发展总目标，紧扣稳粮、提质、增效、绿色核心任务，持续深耕良种良法推广、科学施肥示范工作，推动农业生产提质增效、提档升级，为稳定粮食生产、壮大特色农业、助力乡村产业振兴提供有力支撑。”","淮南市潘集区人民政府 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保护视力色：\n\n2026年10月12日，我院的安徽省财政农业科技成果转化项目“粮油农文旅融合集成技术转化应用”，在黄山市休宁县商山镇荪田村实施的水稻新品种“银月丝苗”百亩示范片进行现场测产验收。该项目由农业机械装备与工程所牵头、联合水稻研究所共同实施。\n\n专家组听取了实施情况汇报，实地考察了项目示范片并机收测产。示范片面积331亩，种植了水稻新品种“银月丝苗”，配套应用了水稻机直播、机插同步侧深施肥、稻田杂草绿色防控、优质稻病虫害绿色防控等绿色高产优质栽培技术。示范片处于黄熟期，专家组随机选取代表性田块，实收折算干谷亩产达605.2公斤。示范片单产比去年黄山市统计的水稻平均亩产460.0公斤高出145.2公斤，增产幅度达31.6%，远超项目增产50公斤的预期目标。\n\n示范片田间生长整齐平衡，熟相好，未发现明显病虫草害，充分展现了该品种高产、优质、抗倒伏能力较强的综合优势，项目区生态环境优良，稻米产量高、品质优，同时展现了稻丰田园的大地景观，为游客初秋乡村旅游和美食体验提供了良好去处。","安徽省农业科学院 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