[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3195":3,"related-3195":53},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":52},3195,"Segmentation of Cactus Diseases Using Machine and Deep Learning)","https:\u002F\u002Fdoi.org\u002F10.37394\u002F232018.2026.14.50","Artificial intelligence and machine learning play a critical role in plant disease detection and management. This study proposes an approach for diagnosing and segmenting cactus diseases using Random Forest (RF) and compares its performance with SVM, ANN, KNN, CNN, and U-Net models. A dataset of 20 cactus images (10 cochineal and 10 black bacterial soft rot), resized to 720×720 pixels, was used. Experimental results show that U-Net achieved the highest accuracy, reaching 98% for cochineal and 94% for black bacterial soft rot. RF also demonstrated strong performance, achieving 92% and 91%, respectively, outperforming several traditional machine learning models. These results confirm the effectiveness of U-Net for accurate segmentation and the suitability of RF for reliable disease detection with limited datasets, supporting their application in precision agriculture.","人工智能与机器学习在植物病害检测与管理中发挥着关键作用。本研究提出了一种利用随机森林（Random Forest, RF）诊断和分割仙人掌病害的方法，并将其性能与SVM、ANN、KNN、CNN和U-Net模型进行了比较。研究使用了20张仙人掌图像数据集（10张胭脂虫病害和10张黑细菌性软腐病），图像尺寸调整为720×720像素。实验结果表明，U-Net取得了最高准确率，胭脂虫病害达到98%，黑细菌性软腐病达到94%。随机森林同样表现出强劲性能，分别达到92%和91%，优于多种传统机器学习模型。这些结果证实了U-Net在精确分割方面的有效性，以及随机森林在有限数据集下进行可靠病害检测的适用性，支持其在精准农业中的应用。",null,"WSEAS TRANSACTIONS ON COMPUTER RESEARCH","2026-09-21T00:00:00Z","论文",10,false,62,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":21,"comment":22},8,18,14,12,1,"方法对比扎实但数据集仅20张图像，属细分作物病害识别的技术验证，产业影响有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","植物病害识别","图像分割","仙人掌",[32,33],"仙人掌 病害 图像分割","U-Net 胭脂虫 黑腐病","仙人掌病害图像分割-3195",0,"10.37394\u002F232018.2026.14.50",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":6,"card":45,"direction":49,"ingested_from":51},"W7213945610",[40,42],{"name":41,"orcid":9},"Aissam El Ibrahimi",{"name":43,"orcid":44},"Nabil El Akchioui","https:\u002F\u002Forcid.org\u002F0000-0001-9808-932X",{"tldr":46,"method":47,"finding":48,"direction":49,"opportunity":50},"该研究用随机森林和多种深度学习模型对仙人掌病害进行分割诊断，并比较性能。","使用20张仙人掌图像，对比RF、SVM、ANN、KNN、CNN和U-Net模型。","U-Net分割准确率最高（胭脂虫98%，黑细菌软腐病94%），RF在有限数据下表现良好。","农业人工智能与决策模型","可探索小样本条件下传统机器学习与深度学习融合的病害分割方法，并扩展至更多作物和病害类型。","openalex","2026-09-22T23:30:39.786818Z",{"total":54,"page":21,"page_size":54,"items":55},6,[56,99,130,166,207,241],{"id":57,"title":58,"url":59,"summary":60,"summary_zh":61,"content":9,"source_name":62,"source_url":59,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":63,"score_detail":64,"sources":67,"tags":69,"search_phrases":72,"slug":75,"view_count":35,"doi":76,"paper":77,"created_at":98},3159,"A multi-agent multi-modal LLM with Monte Carlo tree search for interpretable plant disease classification","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1937679","In plant disease classification tasks, existing task specific models for plant disease classification typically achieve high accuracy by employing tailored network architectures. However, these models are unable to provide reasoning chains, which reduces interpretability and undermines trustworthiness in practical applications. In addition, although multimodal large language models (MLLMs) have made significant progress in smart agriculture, their disease classification accuracy lags behind that of task-specific models due to insufficient domain knowledge. While fine-tuning can enhance domain-specific knowledge, relying solely on unimodal data or simple multimodal information remains insufficient for adequately improving classification accuracy. Specifically, the simple multimodal information in this context refers to a single global description of the entire image produced by an MLLM. To address these issues, this paper proposes a multi-agent collaborative classification framework that integrates Monte Carlo Tree Search (MCTS) with multimodal enhancement. With an MLLM as the core, the framework first introduces an MCTSguided multi-agent collaboration mechanism that directs the agents to progressively generate region-specific descriptive textual information, which together with the image forms complex multimodal information and thereby boosts classification performance. Meanwhile, the search trajectories generated by MCTS are leveraged to construct reasoning processes for enhanced interpretability. Finally, a self-training mechanism is adopted to iteratively optimize the model, further improving overall performance. Experimental results show that the framework improves the baseline MLLM’s accuracy while providing interpretable reasoning chains, achieving gains from 0.6701 to 0.9965 on 3-class potato, 0.5340 to 0.9651 on 5-class potato, 0.5387 to 0.9467 on tomato, and 0.4705 to 0.9948 on grape.","在植物病害分类任务中，现有的植物病害分类专用模型通常通过采用定制化的网络架构来实现高准确率。然而，这些模型无法提供推理链，这降低了可解释性，并在实际应用中削弱了可信度。此外，尽管多模态大语言模型（MLLMs）在智慧农业领域取得了显著进展，但由于领域知识不足，其病害分类准确率仍落后于专用模型。虽然微调可以增强领域特定知识，但仅依赖单模态数据或简单的多模态信息仍不足以充分提升分类准确率。具体而言，此处的简单多模态信息是指MLLM对整个图像生成的单一全局描述。为解决上述问题，本文提出了一种融合蒙特卡洛树搜索（MCTS）与多模态增强的多智能体协同分类框架。该框架以MLLM为核心，首先引入MCTS引导的多智能体协同机制，引导各智能体逐步生成区域特定的描述性文本信息，这些信息与图像共同构成复杂的多模态信息，从而提升分类性能。同时，利用MCTS生成的搜索轨迹构建推理过程，以增强可解释性。最后，采用自训练机制迭代优化模型，进一步提升整体性能。实验结果表明，该框架在提升基线MLLM准确率的同时提供了可解释的推理链，在三分类马铃薯上从0.6701提升至0.9965，在五分类马铃薯上从0.5340提升至0.9651，在番茄上从0.5387提升至0.9467，在葡萄上从0.4705提升至0.9948。","Frontiers in Plant Science",80,{"impact":18,"substance":65,"depth":18,"authority":19,"freshness":17,"relevant":21,"comment":66},22,"提出多智能体+MCTS+多模态增强的可解释植物病害分类框架，准确率大幅提升，方法新颖且结论可靠，对智慧农业AI应用有参考价值。",[68],{"name":62,"url":59},[26,27,70,28,71],"可解释AI","多模态大模型",[73,74],"多智能体 蒙特卡洛树搜索 植物病害分类","多模态大模型 马铃薯 番茄 葡萄 病害识别","多智能体蒙特卡洛树搜索植物病害分类-3159","10.3389\u002Ffpls.2026.1937679",{"doi":76,"openalex_id":78,"authors":79,"venue":62,"cited_by_count":35,"oa_url":59,"card":92,"direction":97,"ingested_from":51},"W7213924931",[80,83,85,87,90],{"name":81,"orcid":82},"Ridong Wu","https:\u002F\u002Forcid.org\u002F0000-0003-0729-3099",{"name":84,"orcid":9},"Hua Zou",{"name":86,"orcid":9},"Zhijie Li",{"name":88,"orcid":89},"Qian Zhou","https:\u002F\u002Forcid.org\u002F0000-0001-7964-8130",{"name":91,"orcid":9},"一子 江﨑",{"tldr":93,"method":94,"finding":95,"direction":49,"opportunity":96},"提出多智能体多模态LLM结合蒙特卡洛树搜索，实现可解释的植物病害分类。","MCTS引导多智能体生成区域描述文本，构建复杂多模态信息并自训练优化。","分类准确率大幅提升，如马铃薯三分类从0.6701升至0.9965，并提供推理链。","可探索将MCTS多智能体框架迁移至其他作物病害或田间复杂场景，提升可解释性与泛化性。","智慧农业 \u002F 农业物联网","2026-09-22T23:30:11.045394Z",{"id":100,"title":101,"url":102,"summary":103,"summary_zh":9,"content":9,"source_name":104,"source_url":9,"published_at":105,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":106,"score_detail":107,"sources":111,"tags":113,"search_phrases":116,"slug":119,"view_count":35,"doi":9,"paper":120,"created_at":129},3119,"CropRowSeg: A Lightweight Model for Seedling Crop Row Image Segmentation Combining Multi-scale CNN and Transformer（融合多尺度 CNN 与 Transformer 的苗期作物行图像轻量化分割模型）","https:\u002F\u002Fnews.cau.edu.cn\u002Fkxyj\u002F9f1540e749e745f29f11355eac1c9444.htm","中国农业大学农机装备智能化设计与制造高水平创新团队翟志强副教授在国际学术期刊《Agriculture Communications》发表研究论文。中国农业大学为唯一署名单位，硕士研究生田永浩为论文第一作者、翟志强为通讯作者。研究面向农机对行作业和作物表型观测对作物行视觉感知的实际需求，提出从图像高效标注、模型架构设计到模型训练的系统解决方案：从数据标注、模型架构、损失函数三个维度构建完整的作物行图像分割技术体系；提出基于中心线引导的条带标注方法仅需人工标注冠层中心线两个端点即可自动生成分割掩码；CropRowSeg 模型采用编码器—解码器架构（编码器 CNN+ViT 双分支结构+通道-空间混合注意力机制，解码器残差连接与坐标注意力结合的渐进式解码架构）。研究可为大田作物感知提供视觉基础模型，为研发农业人工智能专用模型提供借鉴思路。","中国农业大学","2026-09-18T12:39:00Z",78,{"impact":18,"substance":108,"depth":109,"authority":19,"freshness":17,"relevant":21,"comment":110},21,17,"中国农业大学团队提出轻量化作物行分割模型CropRowSeg，方法体系完整、创新点明确，对农机对行作业与作物表型感知有实用价值，值得进入每日精选。",[112],{"name":104,"url":102},[26,27,114,115,29],"农机导航","作物行识别",[117,118],"中国农业大学 翟志强 作物行分割","CropRowSeg 苗期作物行","中国农业大学翟志强作物行分割-3119",{"doi":9,"openalex_id":9,"authors":121,"venue":9,"cited_by_count":35,"oa_url":9,"card":122,"direction":126,"ingested_from":128},[],{"tldr":123,"method":124,"finding":125,"direction":126,"opportunity":127},"提出轻量级作物行分割模型CropRowSeg，并设计中心线引导的条带标注方法。","多尺度CNN与ViT双分支编码器、混合注意力、渐进式解码器，中心线引导标注。","模型实现高效作物行图像分割，降低标注成本，为农机对行作业提供视觉基础。","农业遥感与作物表型","可探索该轻量模型在边缘设备上的实时部署及多作物、多生长阶段的泛化能力。","agent","2026-09-22T00:05:37.930057Z",{"id":131,"title":132,"url":133,"summary":134,"summary_zh":135,"content":9,"source_name":136,"source_url":133,"published_at":137,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":138,"score_detail":139,"sources":143,"tags":145,"search_phrases":148,"slug":151,"view_count":35,"doi":152,"paper":153,"created_at":165},3019,"PSPE-UNet: Projection-based Similarity Prototype Embedding UNet for Apple Leaf Disease Segmentation","https:\u002F\u002Fdoi.org\u002F10.22266\u002Fijies2026.1031.18","Apple leaf disease segmentation plays a significant role in precision agriculture by enabling the accurate identification and localization of infected regions at the pixel level.However, diverse apple leaf diseases exhibit similar symptoms, such as overlapping lesions makes it challenging to distinguish between various disease classes.In this research, a Projection-based Similarity Prototype Embedding UNet (PSPE-UNet) is proposed to segment apple leaf diseases.Employing a projection head with a similarity prototype embedding in UNet enhances feature discrimination by mapping pixel-level representations into a normalized embedding space.This ensures better separation between healthy and disease regions, even when the regions exhibit similar texture and chromatic characteristics.Three learnable prototypes corresponding to healthy, disease, and boundary regions are used.The boundary prototype act as learnable auxiliary feature prototype within the auxiliary boundary branch to compute boundary probability map during training while disease prediction is based on healthy and disease prototypes.In addition, this method enhances the boundary delineation for irregular and small lesions by refining the feature alignment.Hence, the proposed PSPE-UNet achieves a high Pixel Accuracy (PA) of 98.96%, which is compared to existing methods such as the AS-DeepLabV3+ on the Apple Tree Leaf Disease Segmentation Dataset (ATLDSD).Moreover, proposed PSPE-UNet obtains an inference time of 0.0217s per batch (8 images), corresponding to 0.0027s per image on ATLDSD dataset compared to traditional methods like UNet.","苹果叶片病害分割在精准农业中具有重要意义，能够在像素级别上准确识别和定位感染区域。然而，不同苹果叶片病害表现出相似的症状，例如病灶重叠使得区分不同病害类别具有挑战性。本研究提出了一种基于投影的相似性原型嵌入UNet（PSPE-UNet）用于苹果叶片病害分割。在UNet中采用带有相似性原型嵌入的投影头，通过将像素级表示映射到归一化嵌入空间来增强特征判别能力。这确保了健康和病害区域之间更好的分离，即使这些区域表现出相似的纹理和色彩特征。使用三个可学习原型分别对应健康、病害和边界区域。边界原型在辅助边界分支中作为可学习辅助特征原型，在训练期间计算边界概率图，而病害预测则基于健康和病害原型。此外，该方法通过细化特征对齐增强了对不规则和小病灶的边界描绘。因此，所提出的PSPE-UNet在苹果树叶病害分割数据集（ATLDSD）上达到了98.96%的高像素精度（PA），并与现有方法如AS-DeepLabV3+进行了比较。此外，所提出的PSPE-UNet在ATLDSD数据集上获得了每批次（8张图像）0.0217秒的推理时间，相当于每张图像0.0027秒，与UNet等传统方法相比具有优势。","International journal of intelligent engineering and systems","2026-09-19T00:00:00Z",70,{"impact":20,"substance":140,"depth":109,"authority":20,"freshness":141,"relevant":21,"comment":142},20,9,"提出基于相似度原型嵌入的UNet分割方法，在苹果叶病害数据集上取得98.96%像素精度，方法新颖、数据明确，但属细分算法研究，产业影响有限。",[144],{"name":136,"url":133},[26,27,146,29,147],"精准农业","苹果病害",[149,150],"PSPE-UNet 苹果叶病害 分割","苹果叶病害 数据集 ATLDSD","PSPE-UNet苹果叶病害分割-3019","10.22266\u002Fijies2026.1031.18",{"doi":152,"openalex_id":154,"authors":155,"venue":136,"cited_by_count":35,"oa_url":133,"card":160,"direction":49,"ingested_from":51},"W7213634285",[156,158],{"name":157,"orcid":9},"Vedamurthy Hadavanahalli Kumaraiah",{"name":159,"orcid":9},"Shrinivasacharya Purohit",{"tldr":161,"method":162,"finding":163,"direction":49,"opportunity":164},"提出PSPE-UNet，用投影相似原型嵌入分割苹果叶片病害区域。","UNet+投影头+可学习原型（健康\u002F病害\u002F边界），ATLDSD数据集。","像素精度98.96%，单图推理0.0027秒，优于AS-DeepLabV3+和UNet。","可探索原型嵌入在相似症状多病害区分及轻量化边缘部署中的泛化能力。","2026-09-20T23:30:34.933307Z",{"id":167,"title":168,"url":169,"summary":170,"summary_zh":171,"content":9,"source_name":172,"source_url":169,"published_at":173,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":174,"score_detail":175,"sources":178,"tags":180,"search_phrases":183,"slug":186,"view_count":35,"doi":187,"paper":188,"created_at":206},2962,"A Multi-Modal Generative Model for Tomato Disease Leaves Understanding","https:\u002F\u002Fdoi.org\u002F10.48550\u002Farxiv.2609.19555","Artificial intelligence for plant disease analysis has advanced from task-specific classifiers to multi-modal models capable of jointly interpreting visual and textual information. However, practical deployment in precision agriculture remains limited because most existing approaches treat disease understanding as isolated prediction tasks, failing to capture the complementary relationships among symptom recognition, severity assessment, and question-driven diagnostic reasoning. In tomato pathology, accurate interpretation of diseased leaves requires more than label prediction; it demands integrating visual symptoms with semantic context to support a comprehensive and explainable understanding. Here, we present SOLAR, a multimodal generative model that understands tomato disease spanning six question-answering tasks. SOLAR learns to align visual features with task-aware language representations by Fusion Expert module based on mixture-of-expert, enabling it to generate contextually relevant answers across diverse diagnostic tasks. By formulating tomato disease analysis as a generative Visual Question Answering (VQA) task, SOLAR provides a flexible framework that supports multi-task inference within a single model while improving performance and cross-task knowledge sharing. We evaluate SOLAR on $41,677$ images, including $216,209$ Question-Answering (QA) pairs to understand tomato leaf disease under both closed and open-ended QA settings. Experimental results show that SOLAR consistently outperforms state-of-the-art vision-only, vision-language, and task-specific models across all tasks, demonstrating superior accuracy, robustness, and multimodal reasoning. These findings highlight the potential of generative multimodal modeling as an effective direction for understanding of plant disease. The code for this study is available at https:\u002F\u002Fgithub.com\u002FEnalisUs\u002FSOLAR.","人工智能用于植物病害分析已从任务专用分类器发展到能够联合解读视觉与文本信息的多模态模型。然而，在精准农业中的实际部署仍然有限，因为大多数现有方法将病害理解视为孤立的预测任务，未能捕捉症状识别、严重程度评估与问题驱动诊断推理之间的互补关系。在番茄病理学中，对病叶的准确解读需要的不仅仅是标签预测；它要求将视觉症状与语义上下文相结合，以支持全面且可解释的理解。在此，我们提出SOLAR，一种多模态生成模型，能够理解番茄病害并涵盖六项问答任务。SOLAR通过基于专家混合的融合专家模块，学习将视觉特征与任务感知的语言表示对齐，使其能够在多样化的诊断任务中生成上下文相关的答案。通过将番茄病害分析表述为生成式视觉问答（VQA）任务，SOLAR提供了一个灵活的框架，支持在单一模型内进行多任务推理，同时提升性能并促进跨任务知识共享。我们在$41,677$张图像上评估SOLAR，其中包括$216,209$个问答（QA）对，以在封闭式和开放式问答设置下理解番茄叶片病害。实验结果表明，SOLAR在所有任务上均持续优于最先进的纯视觉、视觉语言和任务专用模型，展现出更优的准确性、鲁棒性和多模态推理能力。这些发现凸显了生成式多模态建模作为理解植物病害的有效方向的潜力。本研究的代码可在https:\u002F\u002Fgithub.com\u002FEnalisUs\u002FSOLAR获取。","arXiv (Cornell University)","2026-09-17T00:00:00Z",79,{"impact":18,"substance":65,"depth":18,"authority":176,"freshness":17,"relevant":21,"comment":177},13,"提出面向番茄病害的多模态生成式VQA模型SOLAR，在4万余张图像上验证多任务诊断性能，方法新颖、数据规模可观，对智慧植保有参考价值。",[179],{"name":172,"url":169},[26,27,181,28,182],"番茄病害","多模态模型",[184,185],"SOLAR 番茄病害 多模态模型","番茄叶片病害 视觉问答","SOLAR番茄病害多模态模型-2962","10.48550\u002Farxiv.2609.19555",{"doi":187,"openalex_id":189,"authors":190,"venue":172,"cited_by_count":35,"oa_url":169,"card":201,"direction":49,"ingested_from":51},"W7213586905",[191,194,196,198],{"name":192,"orcid":193},"Khang Nguyen Quoc","https:\u002F\u002Forcid.org\u002F0000-0003-4927-4822",{"name":195,"orcid":9},"Minh-Phuoc Tran",{"name":197,"orcid":9},"Gia-Han Truong",{"name":199,"orcid":200},"Luyl-Da Quach","https:\u002F\u002Forcid.org\u002F0000-0002-5661-4250",{"tldr":202,"method":203,"finding":204,"direction":49,"opportunity":205},"提出多模态生成模型SOLAR，统一理解番茄病害叶片的六类问答任务。","基于混合专家融合模块对齐视觉与任务感知语言，用4万余图像和21万问答对训练。","SOLAR在闭集和开放问答中均超越视觉、视觉语言及任务专用模型，展现更强推理能力。","可探索将生成式多模态VQA扩展到更多作物和田间实时场景，并融合传感器数据。","2026-09-19T23:30:55.245800Z",{"id":208,"title":209,"url":210,"summary":211,"summary_zh":212,"content":9,"source_name":213,"source_url":210,"published_at":214,"category":12,"cover_url":9,"hotness":215,"is_selected":14,"score":216,"score_detail":217,"sources":219,"tags":223,"search_phrases":225,"slug":228,"view_count":35,"doi":229,"paper":230,"created_at":240},2934,"A CNN-Based Approach for Leaf Disease Prediction in Smart Agriculture","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22825329","Plants play a crucial role in sustaining life by serving as a primary source of energy and mitigating global warming. However, they are increasingly vulnerable to diseases such as bacterial spot, late blight, and Septoria leaf spot, which significantly impact crop yield and agricultural productivity. Early and accurate detection of these diseases is essential for effective disease management and improved agricultural outcomes. This project aims to develop a deep learning-based approach for detecting plant leaf diseases using Convolutional Neural Networks (CNN). By leveraging benchmark datasets, the proposed CNN model demonstrates superior performance compared to traditional machine learning techniques, achieving an accuracy of 92%, precision of 89%, F1-score of 93%, and recall of 92.47%. The results highlight the effectiveness of CNN in automating disease identification, enabling timely intervention, and promoting sustainable agricultural practices.","植物在维持生命方面发挥着至关重要的作用，既是主要的能量来源，又能缓解全球变暖。然而，植物日益受到细菌性斑点病、晚疫病和壳针孢叶斑病等病害的威胁，严重影响作物产量和农业生产率。早期准确地检测这些病害对于有效防控病害和改善农业成果至关重要。本项目旨在开发一种基于深度学习的方法，利用卷积神经网络（CNN）检测植物叶片病害。通过利用基准数据集，所提出的CNN模型展现出优于传统机器学习技术的性能，达到了92%的准确率、89%的精确率、93%的F1分数和92.47%的召回率。结果表明，CNN在自动化病害识别方面具有显著效果，能够实现及时干预并促进可持续农业实践。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-18T00:00:00Z",25,65,{"impact":20,"substance":18,"depth":19,"authority":20,"freshness":141,"relevant":21,"comment":218},"基于CNN的叶片病害识别研究，方法常规、数据集为公开基准，准确率92%属中等水平，对智慧农业植保场景有一定参考价值但缺乏突破性。",[220,221],{"name":213,"url":210},{"name":213,"url":222},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22825330",[26,27,224,28],"深度学习",[226,227],"CNN 植物叶片病害 识别","卷积神经网络 作物病害 检测","CNN植物叶片病害识别-2934","10.5281\u002Fzenodo.22825329",{"doi":229,"openalex_id":231,"authors":232,"venue":213,"cited_by_count":35,"oa_url":210,"card":235,"direction":97,"ingested_from":51},"W7213587327",[233],{"name":234,"orcid":9},"B.Yashmal Sai, K.Karthik, K.Neeraj, G. Mahabub Subhani",{"tldr":236,"method":237,"finding":238,"direction":49,"opportunity":239},"用CNN对植物叶片病害进行自动识别，在基准数据集上取得92%准确率。","基于卷积神经网络，使用植物叶片病害基准数据集训练与评估。","CNN优于传统机器学习方法，准确率92%、F1值93%，可支持及时干预。","可探索轻量化CNN在田间移动端实时检测，并结合多病害与早期症状识别。","2026-09-19T23:30:11.855376Z",{"id":242,"title":243,"url":244,"summary":245,"summary_zh":9,"content":9,"source_name":246,"source_url":9,"published_at":173,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":247,"score_detail":248,"sources":250,"tags":252,"search_phrases":254,"slug":257,"view_count":35,"doi":9,"paper":258,"created_at":265},2901,"AgriScope:面向农业图像的像素级多模态理解统一框架,arXiv 2609.20325(预印本)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.20325","Boudiaf、Alanssari、Hussain、Javed提出AgriScope,一个统一的像素级多模态农业图像理解框架,联合支持图像级、区域级、像素级理解,可实现接地描述生成、指代表达分割、多轮多模态交互等任务。集成生物专用语义表征、密集空间表征与像素解码;引入大规模像素级农业多模态指令调优数据集AgriGround,包含50万+图像和1100万+指令跟随样本,涵盖植物病害分析、作物与杂草识别、昆虫识别、细粒度植物理解。实验表明AgriScope在多项农业视觉语言任务上有效。","arXiv (preprint)",75,{"impact":18,"substance":65,"depth":18,"authority":17,"freshness":141,"relevant":21,"comment":249},"提出统一像素级农业多模态理解框架并开源50万图像、1100万指令样本的大规模数据集，方法新颖、数据规模突出，但为arXiv预印本、未经同行评审，权威性有限，值得作为前沿技术动态精选。",[251],{"name":246,"url":244},[26,27,253,28,71],"农业遥感",[255,256],"AgriScope 农业图像 多模态","AgriGround 像素级 农业数据集","AgriScope农业图像多模态-2901",{"doi":9,"openalex_id":9,"authors":259,"venue":9,"cited_by_count":35,"oa_url":9,"card":260,"direction":49,"ingested_from":128},[],{"tldr":261,"method":262,"finding":263,"direction":49,"opportunity":264},"提出AgriScope统一框架，实现农业图像像素级多模态理解与多任务交互。","构建AgriGround数据集（50万+图像、1100万+指令样本），融合语义与","AgriScope在接地描述、指代分割、多轮交互等农业视觉语言任务上有效。","可探索像素级多模态模型在田间实时病害诊断与精准施药决策中的落地与轻量化。","2026-09-19T00:06:08.678379Z"]