[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2519":3,"related-2519":89},{"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,"primary_topic":9,"secondary_topics":31,"classification":32,"search_phrases":63,"slug":66,"view_count":21,"doi":67,"paper":68,"created_at":88},2519,"OneGrow: Unified Temporal Plant Image and Mask Generation","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.09.11.750899","Image-based crop phenotyping benefits from image series that capture plant development together with organ-level labels. Such paired data are limited because organ annotation is expensive, and following the same plants over time requires repeated, registered imaging. Existing generative models for plants either synthesize temporal imagery without structural labels or generate labeled images without a temporal dimension. We introduce OneGrow, a latent flow-matching model that jointly models wheat images and their organ-segmentation masks over time. Images and masks share a single frozen image autoencoder. A reveal specifies which content is observed context, so the same model covers tasks such as mask-to-image synthesis, image-to-mask segmentation, and temporal forecasting. For sequences longer than the training window, a sliding-window roll-out generates each new image from the preceding frames, keeping long sequences temporally consistent. We train jointly on a large single-frame wheat dataset and a multi-year temporal dataset, using pseudo-labels from a pretrained segmentation model. We evaluate segmentation and image quality against held-out references and assess the multi-task and temporal behavior qualitatively.","基于图像的作物表型分析受益于能够同时捕捉植物发育过程与器官级标签的图像序列。此类配对数据较为有限，因为器官标注成本高昂，且对同一植株进行长期追踪需要重复的配准成像。现有的植物生成模型要么合成不具备结构标签的时间序列图像，要么生成不具备时间维度的标注图像。我们提出OneGrow，一种潜在流匹配模型，可联合建模小麦图像及其器官分割掩码随时间的演变。图像与掩码共享一个冻结的图像自编码器。通过指定哪些内容作为观测上下文，同一模型即可覆盖掩码到图像合成、图像到掩码分割以及时间预测等任务。对于超出训练窗口的序列，采用滑动窗口展开方式，由前序帧生成每一新图像，从而保持长序列的时间一致性。我们在一个大型单帧小麦数据集和一个多年时间数据集上进行联合训练，使用预训练分割模型生成的伪标签。我们以留出参考数据评估分割和图像质量，并定性评估多任务与时间行为。",null,"bioRxiv (Cold Spring Harbor Laboratory)","2026-09-14T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,21,13,8,1,"面向小麦时序图像与器官分割掩码联合生成的流匹配模型，方法新颖、数据规模可观，对作物表型自动化标注有实质推动，值得进入每日精选。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","小麦","作物表型","图像生成",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":41,"reason":62},[38,46,52,57],{"code":39,"score":40,"supported":41,"title":42,"abstract":43,"tags":45},"A2",7,true,[],[28,44],"wheat",[28],{"code":47,"score":40,"supported":41,"title":48,"abstract":49,"tags":51},"I3",[],[29,50],"crop phenotyping",[29],{"code":53,"score":21,"supported":14,"title":54,"abstract":55,"tags":56},"I1",[],[],[26],{"code":58,"score":21,"supported":14,"title":59,"abstract":60,"tags":61},"I2",[],[],[27],"多个产业证据相当，需要确认主归属",[64,65],"农业人工智能 作物表型 图像生成 智慧农业","农业人工智能 作物表型","农业人工智能作物表型图像生成智慧农业-2519","10.64898\u002F2026.09.11.750899",{"doi":67,"openalex_id":69,"authors":70,"venue":10,"cited_by_count":80,"oa_url":6,"card":81,"direction":85,"ingested_from":87},"W7212556016",[71,74,77],{"name":72,"orcid":73},"Mike Boss","https:\u002F\u002Forcid.org\u002F0000-0002-7234-3796",{"name":75,"orcid":76},"Michele Volpi","https:\u002F\u002Forcid.org\u002F0000-0003-2771-0750",{"name":78,"orcid":79},"Lukas Roth","https:\u002F\u002Forcid.org\u002F0000-0003-1435-9535",0,{"tldr":82,"method":83,"finding":84,"direction":85,"opportunity":86},"提出OneGrow模型，联合生成小麦时序图像与器官分割掩码。","潜在流匹配模型，共享冻结自编码器，滑动窗口滚动生成。","同一模型可完成掩码到图像、图像分割和时序预测，保持长序列一致。","农业遥感与作物表型","可探索多作物、多器官的时序联合生成，降低表型标注成本。","openalex","2026-09-15T23:30:15.715938Z",{"total":90,"page":21,"page_size":90,"items":91},6,[92,144,247,337,396,452],{"id":93,"title":94,"url":95,"summary":96,"summary_zh":97,"content":9,"source_name":98,"source_url":95,"published_at":99,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":100,"score_detail":101,"sources":104,"tags":106,"primary_topic":9,"secondary_topics":109,"classification":110,"search_phrases":128,"slug":131,"view_count":80,"doi":132,"paper":133,"created_at":143},5317,"PLANT-GPK v4.0: A Unified Time-Discovery Framework for Multi-Stage Plant Phenotyping","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23460911","This paper presents a comprehensive extension to the PLANT-GPK framework, demonstrating its robust capability to analyze plants across entirely different developmental stages from single images. By formulating an inverse problem that simultaneously optimizes physiological parameters and the hidden time dimension (t), the model successfully discovered the exact age of two distinct wheat samples: a vegetative (green) stage and a senescent (golden) stage. The framework bridges computer vision, spatial heterogeneity analysis, and ODE-based physics to provide a complete digital twin capable of forecasting future harvest windows regardless of the plant's current state.","本文对PLANT-GPK框架进行了全面扩展，展示了其仅凭单张图像即可分析处于完全不同发育阶段植物的稳健能力。通过构建一个同时优化生理参数与隐藏时间维度（t）的反问题，该模型成功发现了两个不同小麦样本的精确年龄：一个处于营养期（绿色阶段），另一个处于衰老期（金色阶段）。该框架融合了计算机视觉、空间异质性分析以及基于常微分方程（ODE）的物理学，提供了一个完整的数字孪生体，无论植物当前处于何种状态，均能预测未来的收获窗口。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-19T00:00:00Z",81,{"impact":17,"substance":102,"depth":17,"authority":19,"freshness":13,"relevant":21,"comment":103},22,"该研究提出统一时间发现框架，可从单幅图像反演植物生育阶段并预测收获窗口，方法新颖且具数字孪生应用潜力，值得进入每日精选。",[105],{"name":98,"url":95},[26,27,28,107,108],"数字孪生","植物表型",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":111},{"candidates":112,"agricultural_evidence":41,"reason":62},[113,118,122],{"code":53,"score":20,"supported":41,"title":114,"abstract":115,"tags":117},[],[107,116],"digital twin",[107,26],{"code":39,"score":40,"supported":41,"title":119,"abstract":120,"tags":121},[],[28,44],[28],{"code":58,"score":40,"supported":41,"title":123,"abstract":124,"tags":127},[],[125,126],"计算机视觉","computer vision",[27],[129,130],"PLANT-GPK 植物表型","小麦 数字孪生 生育期","PLANT-GPK植物表型-5317","10.5281\u002Fzenodo.23460911",{"doi":132,"openalex_id":134,"authors":135,"venue":98,"cited_by_count":80,"oa_url":95,"card":138,"direction":85,"ingested_from":87},"W7222895088",[136],{"name":137,"orcid":9},"MOHAMMED TAHAR SAIM",{"tldr":139,"method":140,"finding":141,"direction":85,"opportunity":142},"扩展PLANT-GPK框架，从单张图像反演植物生理参数与隐藏时间维度，实现跨发育阶段表型分析。","基于ODE物理模型的反问题优化，联合估计生理参数与时间t，用两个小麦样本验证。","模型成功发现营养期与衰老期小麦的精确年龄，可预测收获窗口。","单图跨阶段时间反演可延伸至多作物多生育期数字孪生与收获期预测的通用化研究。","2026-10-19T23:30:19.498798Z",{"id":145,"title":146,"url":147,"summary":148,"summary_zh":149,"content":9,"source_name":150,"source_url":147,"published_at":151,"category":12,"cover_url":9,"hotness":152,"is_selected":14,"score":153,"score_detail":154,"sources":157,"tags":162,"primary_topic":165,"secondary_topics":170,"classification":171,"search_phrases":219,"slug":222,"view_count":80,"doi":223,"paper":224,"created_at":246},4814,"Wearable Sensors for Smart Agriculture: From Sensing Mechanisms to Crop Phenotyping and Harvesting Applications","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriculture16202210","Global food security faces severe pressures from population growth, climate change and resource shortages. Traditional agricultural monitoring relies on manual sampling and offline laboratory testing, which involve considerable time delays and cannot realize continuous, in situ dynamic tracking of crop physiological status, field environment and agricultural product quality. Among the various sensing platforms for agriculture, flexible wearable sensors represent an important branch, featuring light weight, conformability, low power consumption and real-time response. It should be noted that rigid-substrate and silicon-based sensors remain indispensable in many agricultural applications, and this review focuses specifically on flexible wearable sensors while acknowledging the broader landscape of agricultural sensing technologies. Through conformal contact with plant surfaces or wearable integration, they can convert physical, chemical and biological signals of agroecosystems into readable electrical\u002Foptical signals via specific sensing transduction mechanisms. This review systematically categorizes wearable sensors for smart agriculture into two main types, optical and electrical, and elaborates their intrinsic sensing principles, summarizes typical application scenarios covering crop growth monitoring, post-harvest harvesting and agricultural product safety detection, and introduces the full-flow signal preprocessing, feature extraction and intelligent analysis technologies matched with wearable sensing data. Finally, the bottlenecks restricting large-scale field deployment of current wearable sensors are summarized, and the development directions of multi-modal integrated wearable sensing, biomimetic nanosensors inspired by natural plant sensing systems, and artificial intelligence (AI)-assisted agricultural monitoring platforms are prospected, aiming to provide a theoretical reference for the design optimization and popularization of agricultural wearable sensing equipment.","全球粮食安全面临人口增长、气候变化和资源短缺的严峻压力。传统农业监测依赖人工采样和离线实验室检测，存在较大的时间延迟，无法实现对作物生理状态、田间环境和农产品品质的连续、原位动态追踪。在各类农业传感平台中，柔性可穿戴传感器是一个重要分支，具有轻量、共形、低功耗和实时响应等特点。需要指出的是，刚性基底和硅基传感器在许多农业应用中仍不可或缺，本综述聚焦于柔性可穿戴传感器，同时承认农业传感技术的更广泛格局。通过与植物表面的共形接触或可穿戴集成，柔性可穿戴传感器能够借助特定的传感转导机制，将农业生态系统的物理、化学和生物信号转化为可读的电信号\u002F光信号。本综述将面向智慧农业的可穿戴传感器系统性地分为光学和电学两大类，阐述其内在传感原理，总结涵盖作物生长监测、采后收获和农产品安全检测的典型应用场景，并介绍与可穿戴传感数据匹配的全流程信号预处理、特征提取和智能分析技术。最后，总结了制约当前可穿戴传感器大规模田间部署的瓶颈，并展望了多模态集成可穿戴传感、受自然植物传感系统启发的仿生纳米传感器以及人工智能（AI）辅助农业监测平台的发展方向，旨在为农业可穿戴传感设备的设计优化与推广应用提供理论参考。","Agriculture","2026-10-11T00:00:00Z",25,77,{"impact":17,"substance":155,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":156},20,"系统综述柔性可穿戴传感在智慧农业中的机理与应用，方法梳理扎实，对农业信息化领域有参考价值。",[158,159],{"name":150,"url":147},{"name":160,"url":161},"MDPI Agriculture 16(20) 2210 2026-10-11","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F20\u002F2210",[26,27,29,163,164],"农产品安全","可穿戴传感器",{"code":47,"slug":166,"name":167,"path":168,"parent_code":169},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":172,"locked":14,"version":34,"method":35,"evidence":173},"classified",{"candidates":174,"agricultural_evidence":41},[175,181,186,193,200,206,212],{"code":53,"score":176,"supported":41,"title":177,"abstract":179,"tags":180},15,[178],"smart agriculture",[26,178],[26],{"code":47,"score":182,"supported":41,"title":183,"abstract":184,"tags":185},9,[50],[],[29],{"code":187,"score":90,"supported":41,"title":188,"abstract":189,"tags":192},"A8",[],[190,191],"粮食安全","food security",[],{"code":194,"score":90,"supported":41,"title":195,"abstract":196,"tags":199},"H7",[],[197,198],"气候变化","climate change",[],{"code":58,"score":201,"supported":41,"title":202,"abstract":203,"tags":205},4,[],[204],"artificial intelligence",[27],{"code":207,"score":201,"supported":41,"title":208,"abstract":209,"tags":211},"I5",[],[210],"农业传感",[164],{"code":213,"score":214,"supported":41,"title":215,"abstract":216,"tags":218},"H6",3,[],[217],"农业生态",[],[220,221],"可穿戴传感器 智慧农业","作物表型 传感器","可穿戴传感器智慧农业-4814","10.3390\u002Fagriculture16202210",{"doi":223,"openalex_id":225,"authors":226,"venue":150,"cited_by_count":80,"oa_url":147,"card":240,"direction":245,"ingested_from":87},"W7221545913",[227,229,232,234,236,238],{"name":228,"orcid":9},"Liuyang Qiu",{"name":230,"orcid":231},"Xindong Wang","https:\u002F\u002Forcid.org\u002F0000-0002-8101-7300",{"name":233,"orcid":9},"Xinxin Wu",{"name":235,"orcid":9},"Jie Yu",{"name":237,"orcid":9},"Hao Li",{"name":239,"orcid":9},"Xinai Zhang",{"tldr":241,"method":242,"finding":243,"direction":85,"opportunity":244},"综述柔性可穿戴传感器在智慧农业中的传感机制、作物表型监测与采收应用。","综述光学与电学两类柔性可穿戴传感器原理、应用及信号智能分析技术。","可穿戴传感器可实现作物生理、环境与品质的连续原位监测，但田间规模化部署仍受限。","多模态集成、仿生纳米传感与AI辅助平台是突破田间规模化监测瓶颈的关键方向。","智慧农业 \u002F 农业物联网","2026-10-13T23:30:12.054798Z",{"id":248,"title":249,"url":250,"summary":251,"summary_zh":252,"content":9,"source_name":253,"source_url":250,"published_at":254,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":255,"score_detail":256,"sources":260,"tags":262,"primary_topic":265,"secondary_topics":269,"classification":270,"search_phrases":306,"slug":309,"view_count":214,"doi":310,"paper":311,"created_at":336},4040,"SPATIAL AND CLIMATE DATA ANALYSIS FOR MACHINE LEARNING-BASED CROP YIELD PREDICTION IN NORTHERN KAZAKHSTAN","https:\u002F\u002Fdoi.org\u002F10.32014\u002F2026.2518-1726.462","Crop yield prediction is important for food security, agricultural planning, and effective resource management, particularly in Northern Kazakhstan, where agricultural production is strongly influenced by climate variability. Changes in temperature and precipitation can affect crop productivity and cause considerable differences in yield between years. The aim of this study is to develop, compare, and evaluate machine learning regression models for wheat yield prediction in the North Kazakhstan Region using climatic data. A dataset covering 1990–2025 was prepared using official crop yield statistics from the Bureau of National Statistics of the Republic of Kazakhstan and climatic data from the NASA POWER database. Daily climatic data were collected from five representative geographical points across the North Kazakhstan Region. The climatic variables included average, minimum, and maximum air temperature and precipitation. Daily temperature values were aggregated into annual mean values, while precipitation was summed to obtain annual totals. The annual values from the five geographical points were then averaged to obtain representative regional indicators. Several regression algorithms were compared, including Linear Regression, Random Forest, Gradient Boosting, Bagging Regressor, Decision Tree, and XGBoost. A hybrid model combining XGBoost and Gradient Boosting was also evaluated. Wheat yield was used as the target variable, while climatic variables were used as predictors. To preserve the temporal structure of the data and avoid information leakage, walk-forward validation was applied for 2020–2025. Model performance was evaluated using R², RMSE, and MAE. XGBoost demonstrated the best predictive performance, achieving R² = 0.811, RMSE = 1.436 c\u002Fha, and MAE = 1.160 c\u002Fha. The results demonstrate the potential of machine learning and climatic data for wheat yield prediction and agricultural decision support in Northern Kazakhstan.","作物产量预测对粮食安全、农业规划和有效资源管理具有重要意义，尤其是在哈萨克斯坦北部，该地区农业生产受气候变异性影响显著。气温和降水的变化会影响作物生产力，并导致年际间产量差异较大。本研究旨在利用气候数据，开发、比较和评估用于哈萨克斯坦北部地区小麦产量预测的机器学习回归模型。研究使用哈萨克斯坦共和国国家统计局提供的官方作物产量统计数据和NASA POWER数据库的气候数据，构建了覆盖1990—2025年的数据集。每日气候数据采集自哈萨克斯坦北部地区五个具有代表性的地理点位。气候变量包括平均气温、最低气温、最高气温和降水量。每日气温值被聚合为年均值，降水量则累加得到年总量。随后，对五个地理点位的年度值取平均，以获得具有代表性的区域指标。研究比较了多种回归算法，包括线性回归、随机森林、梯度提升、Bagging回归器、决策树和XGBoost，并评估了XGBoost与梯度提升相结合的混合模型。小麦产量作为目标变量，气候变量作为预测因子。为保持数据的时间结构并避免信息泄漏，研究对2020—2025年采用了前向滚动验证。模型性能采用R²、RMSE和MAE进行评估。XGBoost表现出最佳的预测性能，达到R² = 0.811、RMSE = 1.436 c\u002Fha、MAE = 1.160 c\u002Fha。结果表明，机器学习和气候数据在哈萨克斯坦北部小麦产量预测和农业决策支持方面具有潜力。","NEWS OF THE NATIONAL ACADEMY OF SCIENCES OF THE REPUBLIC OF KAZAKHSTAN","2026-10-01T00:00:00Z",70,{"impact":257,"substance":155,"depth":258,"authority":19,"freshness":20,"relevant":21,"comment":259},12,17,"基于1990—2025年气候数据与多种机器学习模型的小麦产量预测研究，方法规范、结论可靠，但区域局限于哈萨克斯坦北部，公共影响有限。",[261],{"name":253,"url":250},[26,27,263,28,264],"产量预测","农业气象",{"code":58,"slug":266,"name":267,"path":268,"parent_code":169},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[],{"status":172,"locked":14,"version":34,"method":35,"evidence":271},{"candidates":272,"agricultural_evidence":41},[273,280,284,288,294,298,302],{"code":58,"score":17,"supported":41,"title":274,"abstract":276,"tags":279},[275],"machine learning",[277,275,278],"机器学习","decision support",[27],{"code":39,"score":40,"supported":41,"title":281,"abstract":282,"tags":283},[],[28,44],[28],{"code":187,"score":90,"supported":41,"title":285,"abstract":286,"tags":287},[],[190,191],[],{"code":289,"score":214,"supported":41,"title":290,"abstract":291,"tags":293},"J1",[],[292],"农业规划",[],{"code":194,"score":21,"supported":14,"title":295,"abstract":296,"tags":297},[],[],[264],{"code":53,"score":21,"supported":14,"title":299,"abstract":300,"tags":301},[],[],[26],{"code":47,"score":21,"supported":14,"title":303,"abstract":304,"tags":305},[],[],[263],[307,308],"哈萨克斯坦 小麦 产量预测","NASA POWER 气候数据 机器学习","哈萨克斯坦小麦产量预测-4040","10.32014\u002F2026.2518-1726.462",{"doi":310,"openalex_id":312,"authors":313,"venue":253,"cited_by_count":80,"oa_url":329,"card":330,"direction":334,"ingested_from":87},"W7215105964",[314,317,320,323,326],{"name":315,"orcid":316},"Aigul Mimenbayeva","https:\u002F\u002Forcid.org\u002F0000-0003-4652-470X",{"name":318,"orcid":319},"A A Naizagarayeva","https:\u002F\u002Forcid.org\u002F0000-0001-6888-1092",{"name":321,"orcid":322},"Tleugaisha Ospanova","https:\u002F\u002Forcid.org\u002F0000-0002-1729-1321",{"name":324,"orcid":325},"D. K. Koikelova","https:\u002F\u002Forcid.org\u002F0009-0003-6644-9345",{"name":327,"orcid":328},"Raikhan Kudabayeva","https:\u002F\u002Forcid.org\u002F0009-0002-0827-9395","https:\u002F\u002Fjournals.nauka-nanrk.kz\u002Fphysics-mathematics\u002Farticle\u002Fdownload\u002F8926\u002F5764",{"tldr":331,"method":332,"finding":333,"direction":334,"opportunity":335},"用1990–2025年气候数据与机器学习模型预测哈萨克斯坦北部小麦产量。","NASA POWER气候数据+官方产量统计，比较XGBoost等回归模型并采用前","XGBoost预测效果最佳，R²=0.811，RMSE=1.436 c\u002Fha，MAE=1.160 c","农业人工智能与决策模型","可引入遥感植被指数与土壤数据，构建多源融合模型并迁移至其他气候敏感区。","2026-10-02T23:30:45.440623Z",{"id":338,"title":339,"url":340,"summary":341,"summary_zh":342,"content":9,"source_name":343,"source_url":340,"published_at":254,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":344,"score_detail":345,"sources":349,"tags":351,"primary_topic":353,"secondary_topics":354,"classification":355,"search_phrases":371,"slug":374,"view_count":214,"doi":375,"paper":376,"created_at":395},3963,"MSO-Det: A multi-level synergistic optimization framework for robust spike object detection in field scenes","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112473","MSO-Det: A multi-level synergistic optimization framework for robust spike object detection in field scenes。Computers and Electronics in Agriculture","MSO-Det：一种用于田间场景中鲁棒脉冲目标检测的多级协同优化框架。《计算机与电子农业》","Computers and Electronics in Agriculture",69,{"impact":257,"substance":17,"depth":346,"authority":347,"freshness":182,"relevant":21,"comment":348},16,14,"发表于农业信息领域核心期刊的穗部目标检测新方法，方法有创新但属细分技术进展，公共影响有限。",[350],{"name":343,"url":340},[26,27,352,29],"目标检测",{"code":58,"slug":266,"name":267,"path":268,"parent_code":169},[],{"status":172,"locked":14,"version":34,"method":35,"evidence":356},{"candidates":357,"agricultural_evidence":41},[358,363,367],{"code":58,"score":359,"supported":41,"title":360,"abstract":361,"tags":362},5,[],[352],[27,352],{"code":53,"score":21,"supported":14,"title":364,"abstract":365,"tags":366},[],[],[26],{"code":47,"score":21,"supported":14,"title":368,"abstract":369,"tags":370},[],[],[29],[372,373],"MSO-Det 穗部检测","田间场景 穗目标检测","MSO-Det穗部检测-3963","10.1016\u002Fj.compag.2026.112473",{"doi":375,"openalex_id":377,"authors":378,"venue":343,"cited_by_count":80,"oa_url":9,"card":390,"direction":334,"ingested_from":87},"W7215045657",[379,381,383,385,387],{"name":380,"orcid":9},"Yiqun Wang",{"name":382,"orcid":9},"Shuo Zhang",{"name":384,"orcid":9},"Xiaolong Liu",{"name":386,"orcid":9},"Ze Ji",{"name":388,"orcid":389},"Weikuan Jia","https:\u002F\u002Forcid.org\u002F0000-0001-6242-3269",{"tldr":391,"method":392,"finding":393,"direction":334,"opportunity":394},"提出MSO-Det多级协同优化框架，用于田间场景下鲁棒的穗部目标检测。","多级协同优化框架，结合田间场景图像进行穗目标检测。","该框架提升了田间穗检测的鲁棒性，但摘要未给出具体指标。","可探索多级优化在复杂田间光照与遮挡下的泛化能力及轻量化部署。","2026-10-02T23:30:01.492947Z",{"id":397,"title":398,"url":399,"summary":400,"summary_zh":9,"content":9,"source_name":401,"source_url":399,"published_at":402,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":403,"score_detail":404,"sources":406,"tags":408,"primary_topic":9,"secondary_topics":411,"classification":412,"search_phrases":429,"slug":432,"view_count":214,"doi":433,"paper":434,"created_at":451},3528,"Long-term dynamic root monitoring based on classification fusion and attention mechanism","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.biosystemseng.2026.104602","Long-term dynamic root monitoring based on classification fusion and attention mechanism。Biosystems Engineering","Biosystems Engineering","2026-09-25T00:00:00Z",67,{"impact":257,"substance":17,"depth":346,"authority":19,"freshness":20,"relevant":21,"comment":405},"核心期刊论文，方法上有分类融合与注意力机制创新，属农业人工智能细分进展，但应用范围有限，未达重大突破层级。",[407],{"name":401,"url":399},[26,27,409,29,410],"深度学习","根系监测",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":413},{"candidates":414,"agricultural_evidence":14,"reason":428},[415,420,424],{"code":58,"score":416,"supported":14,"title":417,"abstract":418,"tags":419},2,[],[],[27,409],{"code":53,"score":21,"supported":14,"title":421,"abstract":422,"tags":423},[],[],[26],{"code":47,"score":21,"supported":14,"title":425,"abstract":426,"tags":427},[],[],[29],"农业相关性需要复核",[430,431],"Biosystems Engineering 根系监测","动态根系监测 注意力机制","BiosystemsEngineering根系监测-3528","10.1016\u002Fj.biosystemseng.2026.104602",{"doi":433,"openalex_id":435,"authors":436,"venue":401,"cited_by_count":80,"oa_url":9,"card":9,"direction":9,"ingested_from":87},"W7214313105",[437,439,441,443,445,447,449],{"name":438,"orcid":9},"Yinkai Fu",{"name":440,"orcid":9},"Pengchong Zhang",{"name":442,"orcid":9},"Yue Zhao",{"name":444,"orcid":9},"Qiaoling Han",{"name":446,"orcid":9},"Benye Xi",{"name":448,"orcid":9},"Yandong Zhao",{"name":450,"orcid":9},"Jianhui Lin","2026-09-26T23:30:06.143382Z",{"id":453,"title":454,"url":455,"summary":456,"summary_zh":457,"content":9,"source_name":458,"source_url":455,"published_at":459,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":460,"sources":462,"tags":464,"primary_topic":467,"secondary_topics":472,"classification":473,"search_phrases":492,"slug":495,"view_count":201,"doi":496,"paper":497,"created_at":540},3490,"Balancing accuracy, completeness, and efficiency for rice 3D reconstruction through CBAM-UNet-based multi-view segmentation and camera configuration optimization","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1882534","Multi-view 3D reconstruction has been widely applied in plant phenotyping, but the complex canopy structure of rice plants poses significant challenges for reconstruction accuracy, completene7ss, and efficiency. In this study, we developed an optimized workflow for 3D reconstruction of rice using the self-developed Metatlas V1 multi-view imaging platform combined with the COLMAP + OpenMVS pipeline. A CBAM-UNet-based image segmentation model was used to extract plant regions from complex backgrounds, outperforming thresholding, U-Net, and U-Net++, and increasing the number of reconstructed points. Camera configuration optimization was performed by systematically combining equidistant and greedy strategies, resulting in an optimal six-camera setup (#2, #3, #4, #6, #7, and #8, corresponding to −30°, −15°, 0°, +30°, +45°, and +60° relative to the horizontal viewpoint), which provided complementary views of the canopy inner structure and stem base. This configuration reduced the average nearest-neighbor distance to 0.037–0.066 cm across different varieties and growth stages compared with the full 11-camera setup, retained 73–76% of the points, and decreased reconstruction duration by approximately 70–80%. The strong agreement between point-cloud-derived and manually measured plant height and canopy width ( R 2 = 0.989 and 0.946, respectively) supported the accuracy of the point-cloud-derived phenotypic measurements. Overall, integrating image segmentation with camera-configuration optimization offers an accurate and efficient solution for high-throughput 3D phenotyping of rice using Metatlas V1, balancing reconstruction accuracy, point-cloud completeness, and computational efficiency, and may provide a methodological reference for camera-configuration design in other rotational multi-view phenotyping platforms.","多视角三维重建已广泛应用于植物表型分析，但水稻复杂的冠层结构对重建精度、完整性和效率构成了重大挑战。本研究利用自主研发的Metatlas V1多视角成像平台，结合COLMAP + OpenMVS流程，开发了一套优化的水稻三维重建工作流。采用基于CBAM-UNet的图像分割模型从复杂背景中提取植株区域，其性能优于阈值分割、U-Net和U-Net++，并增加了重建点数量。通过系统组合等距策略和贪心策略进行相机配置优化，得到了最优的六相机方案（#2、#3、#4、#6、#7和#8，分别对应相对于水平视角的−30°、−15°、0°、+30°、+45°和+60°），该方案提供了冠层内部结构和茎基部的互补视角。与完整的11相机方案相比，该配置将不同品种和生育期的平均最近邻距离降至0.037–0.066 cm，保留了73–76%的点云，并将重建时间缩短了约70–80%。点云提取的株高和冠幅与人工测量结果高度一致（R²分别为0.989和0.946），验证了点云表型测量的准确性。总体而言，将图像分割与相机配置优化相结合，为利用Metatlas V1进行水稻高通量三维表型分析提供了一种准确高效的解决方案，在重建精度、点云完整性和计算效率之间取得了平衡，并可为其他旋转式多视角表型平台的相机配置设计提供方法学参考。","Frontiers in Plant Science","2026-09-24T00:00:00Z",{"impact":346,"substance":102,"depth":17,"authority":19,"freshness":182,"relevant":21,"comment":461},"该研究提出结合CBAM-UNet分割与相机配置优化的水稻三维重建流程，方法新颖、数据详实，对高通量作物表型分析有参考价值，但属细分领域进展，时效性高。",[463],{"name":458,"url":455},[26,27,465,29,466],"水稻","三维重建",{"code":468,"slug":469,"name":465,"path":470,"parent_code":471},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[],{"status":172,"locked":14,"version":34,"method":35,"evidence":474},{"candidates":475,"agricultural_evidence":41},[476,480,484,488],{"code":468,"score":176,"supported":41,"title":477,"abstract":478,"tags":479},[469],[465,469],[465],{"code":58,"score":416,"supported":14,"title":481,"abstract":482,"tags":483},[],[],[466,27],{"code":53,"score":21,"supported":14,"title":485,"abstract":486,"tags":487},[],[],[26],{"code":47,"score":21,"supported":14,"title":489,"abstract":490,"tags":491},[],[],[29],[493,494],"Metatlas V1 水稻 三维重建","CBAM-UNet 水稻 图像分割","MetatlasV1水稻三维重建-3490","10.3389\u002Ffpls.2026.1882534",{"doi":496,"openalex_id":498,"authors":499,"venue":458,"cited_by_count":80,"oa_url":455,"card":535,"direction":85,"ingested_from":87},"W7214231705",[500,502,505,507,510,512,514,517,520,523,525,527,530,533],{"name":501,"orcid":9},"Haoyang Zhou",{"name":503,"orcid":504},"Rongjie Chen","https:\u002F\u002Forcid.org\u002F0009-0004-8078-2301",{"name":506,"orcid":9},"Hao Wang",{"name":508,"orcid":509},"Minglu Li","https:\u002F\u002Forcid.org\u002F0000-0003-1751-9418",{"name":511,"orcid":9},"Yongkang Teng",{"name":513,"orcid":9},"Shenghao Ye",{"name":515,"orcid":516},"Menglei Wei","https:\u002F\u002Forcid.org\u002F0009-0004-3070-8023",{"name":518,"orcid":519},"Kun Yu","https:\u002F\u002Forcid.org\u002F0000-0002-0190-8702",{"name":521,"orcid":522},"Pingping Fang","https:\u002F\u002Forcid.org\u002F0000-0002-9911-2963",{"name":524,"orcid":9},"Jing Cao",{"name":526,"orcid":9},"Fanglin Zhu",{"name":528,"orcid":529},"Wenyu Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-3322-9736",{"name":531,"orcid":532},"Ting Sun","https:\u002F\u002Forcid.org\u002F0000-0002-9387-4852",{"name":534,"orcid":9},"Min Jiang",{"tldr":536,"method":537,"finding":538,"direction":85,"opportunity":539},"提出CBAM-UNet分割与相机配置优化结合的水稻多视角三维重建流程，兼顾精度、完整性与效率。","Metatlas V1多视角平台、COLMAP+OpenMVS、CBAM-UNe","最优6相机配置将最近邻距离降至0.037–0.066 cm，保留73–76%点云，重建耗时减少约70","可将相机配置优化策略迁移到其他旋转多视角平台，并探索自适应配置与分割模型联合优化。","2026-09-25T23:30:25.303016Z"]