[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3186":3,"related-3186":62},{"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":61},3186,"Cross-Regional Classification of Rice Cropping Systems Based on Within-Year Seasonal Composition Using Multimodal Remote-Sensing Time Series","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183241","Rice cropping-intensity products indicate the number of rice-growing seasons within a year but cannot reveal the seasonal compositions that define rice cropping systems. This limitation constrains detailed characterization of regional rice production patterns. To address this gap, this study developed a framework for rice cropping-system classification based on combinations of early-, middle-, and late-season rice cultivation. The framework integrates SAR-assisted reconstruction of cloud-contaminated Sentinel-2 time series using Sentinel-1 observations, Phenology-Constrained Time-Series Splicing (PTS) to construct PTS samples for underrepresented rice cropping-system classes, and an Ordered-Cycle Query Transformer (OCQT), which encodes annual-global and chronologically ordered-cycle representations with learnable season-query tokens for eight-class classification. On the spatial-block validation set, OCQT trained using real samples together with PTS samples achieved an overall accuracy of 0.9040, a macro-F1 of 0.8643, and a region-balanced macro-F1 of 0.8275, outperforming OCQT trained using only real samples. Across all four independent regional test sets, this configuration increased weighted-F1 by an average of 3.98 percentage points, with a maximum gain of 9.88 percentage points. This study advances rice monitoring from cropping intensity to explicit characterization of within-year seasonal composition and provides a framework for detailed agricultural monitoring using multi-source remote sensing and ordered-cycle representation learning.","水稻种植强度产品能够指示一年内的水稻种植季数，但无法揭示界定水稻种植系统的季节组成。这一局限制约了对区域水稻生产格局的精细刻画。为弥补这一不足，本研究构建了一个基于早稻、中稻和晚稻种植组合的水稻种植系统分类框架。该框架集成了以下三个部分：利用Sentinel-1观测对受云污染的Sentinel-2时间序列进行SAR辅助重建；采用物候约束时间序列拼接（Phenology-Constrained Time-Series Splicing, PTS）为样本不足的水稻种植系统类别构建PTS样本；以及有序循环查询Transformer（Ordered-Cycle Query Transformer, OCQT），该模型通过可学习的季节查询令牌编码年度全局表示和时间有序循环表示，以实现八类分类。在空间分块验证集上，使用真实样本与PTS样本共同训练的OCQT达到了0.9040的总体精度、0.8643的宏F1和0.8275的区域均衡宏F1，优于仅使用真实样本训练的OCQT。在全部四个独立区域测试集上，该配置将加权F1平均提升了3.98个百分点，最大提升达9.88个百分点。本研究推动了水稻监测从种植强度向年内季节组成的显式刻画迈进，并为利用多源遥感和有序循环表示学习开展精细农业监测提供了框架。",null,"Remote Sensing","2026-09-20T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,9,1,"提出融合SAR与光学时序及有序周期Transformer的水稻种植制度分类框架，方法新颖、精度可靠，对农业遥感监测有实质参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","遥感监测","水稻种植","作物分类","多源遥感",[32,33],"Sentinel-1 Sentinel-2 水稻","水稻种植制度 遥感分类","Sentinel-1Sentinel-2水稻-3186",0,"10.3390\u002Frs18183241",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":6,"card":54,"direction":58,"ingested_from":60},"W7213906853",[40,42,45,47,49,51],{"name":41,"orcid":9},"Jingrou Wang",{"name":43,"orcid":44},"Tingting Lu","https:\u002F\u002Forcid.org\u002F0000-0002-3140-5882",{"name":46,"orcid":9},"Beibei Xue",{"name":48,"orcid":9},"Lu Xu",{"name":50,"orcid":9},"Yanyan Shi",{"name":52,"orcid":53},"Dongping Ming","https:\u002F\u002Forcid.org\u002F0000-0002-3422-7399",{"tldr":55,"method":56,"finding":57,"direction":58,"opportunity":59},"提出多模态遥感时序框架，实现跨区域水稻种植制度八分类。","Sentinel-1\u002F2融合重建、PTS样本拼接与OCQT有序周期Transfo","结合PTS样本的OCQT精度达0.904，跨区加权F1平均提升3.98个百分点。","农业遥感与作物表型","可将有序周期表示学习扩展至其他多季作物制度识别与跨区域泛化研究。","openalex","2026-09-22T23:30:25.478404Z",{"total":63,"page":21,"page_size":63,"items":64},6,[65,95,120,170,215,263],{"id":66,"title":67,"url":68,"summary":69,"summary_zh":9,"content":9,"source_name":70,"source_url":9,"published_at":71,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":72,"sources":75,"tags":77,"search_phrases":81,"slug":84,"view_count":35,"doi":9,"paper":85,"created_at":94},3248,"Crop recommendation in precision agriculture: a systematic literature review of methods, trends, and challenges（精准农业中的作物推荐：方法、趋势与挑战系统综述）","https:\u002F\u002Fm2.mtmt.hu\u002Fapi\u002Fpublication\u002F37471110","MDPI 发表精准农业作物推荐方法系统综述：从183项研究中严格筛选129篇发表于2020-2026年的文章，使用PRISMA协议进行综合分析。研究表明集成学习方法（特别是随机森林和XGBoost）在各种农业数据集的预测性能上具有强大能力；支持向量机、决策树、k近邻等传统ML方法仍被广泛使用；同时CNN和LSTM被用于遥感和时间相关农业分析。最常用的数据集来源是Kaggle，典型输入包括土壤养分（NPK）、土壤pH、天气条件和NDVI、EVI等卫星指数。研究主要研究空白：有限的实时部署、低多数据源集成、低跨区域验证、低模型可解释性。研究表明可扩展、可解释的AI系统对农业实际应用具有重要意义。","MDPI","2026-09-22T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":73,"freshness":13,"relevant":21,"comment":74},13,"基于PRISMA的129篇文献系统综述，梳理作物推荐主流方法与四大研究空白，对农业AI落地有参考价值。",[76],{"name":70,"url":68},[26,78,79,80,27],"农业人工智能","机器学习","作物推荐",[82,83],"精准农业 作物推荐 系统综述","XGBoost 随机森林 作物推荐","精准农业作物推荐系统综述-3248",{"doi":9,"openalex_id":9,"authors":86,"venue":9,"cited_by_count":35,"oa_url":9,"card":87,"direction":91,"ingested_from":93},[],{"tldr":88,"method":89,"finding":90,"direction":91,"opportunity":92},"系统综述129篇2020-2026年文献，梳理精准农业作物推荐的方法、趋势与挑战。","PRISMA协议系统综述，分析183项研究筛选出的129篇文献。","集成学习（随机森林、XGBoost）表现最强，主要空白为实时部署、多源集成、跨区域验证与可解释性。","农业人工智能与决策模型","可探索可解释、可跨区域泛化的实时作物推荐系统，并融合多源遥感与物联网数据。","agent","2026-09-23T00:04:33.331160Z",{"id":96,"title":97,"url":98,"summary":99,"summary_zh":9,"content":100,"source_name":101,"source_url":9,"published_at":102,"category":103,"cover_url":9,"hotness":13,"is_selected":14,"score":104,"score_detail":105,"sources":109,"tags":111,"search_phrases":115,"slug":118,"view_count":35,"doi":9,"paper":9,"created_at":119},3230,"农业科普进校园 科技种子润心田——黑龙江省农科院\"农业科普进校园\"活动走进萧红中学","https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1079072228_121106822","黑龙江省农科院\"农业科普进校园\"活动走进哈尔滨市萧红中学，七位专家把实验室里的科研成果\"翻译\"成生动有趣的科普课堂，围绕食品安全、现代种业、智慧农业、遥感监测、生物防治、健康养殖、未来食品等方向，用图文展示、实物观察、视频演示和互动问答，把硬核科研变成通俗易懂的知识。作为省农科院2026年全国科普月重点活动之一，本次活动紧扣\"科技改变生活 创新赢得未来\"主题，聚焦现代农业科普和青少年科学素养提升，是落实全国科普月\"科普讲堂话前沿\"\"科学文化进基层\"部署、推动农业科普资源进校园的具体实践。","## 农业科普进校园 科技种子润心田 ——省农科院“农业科普进校园”活动走进萧红中学\n\n2026-09-21 17:29 来源: [大东北生活资讯](https:\u002F\u002Fwww.sohu.com\u002F)\n\n发布于：北京市\n\n当“农药去哪了”“玉米的秘密”“未来餐桌”这些话题出现在中学课堂，农业就不再是课本里遥远的名词，而是一场可以看、可以问、可以触摸的科技之旅。\n\n近日，省农科院“农业科普进校园”活动走进哈尔滨市萧红中学。七位专家把实验室里的科研成果“翻译”成生动有趣的科普课堂，带着同学们零距离感受现代农业的魅力。\n\n活动现场，专家们围绕食品安全、现代种业、智慧农业、遥感监测、生物防治、健康养殖、未来食品等方向，用图文展示、实物观察、视频演示和互动问答，把硬核科研变成通俗易懂的知识。同学们在观察、提问、交流中，慢慢理解了“藏粮于技”的意义，也感受到科技给农业带来的改变。\n\n作为省农科院2026年全国科普月重点活动之一，本次活动紧扣“科技改变生活 创新赢得未来”主题，聚焦现代农业科普和青少年科学素养提升，是落实全国科普月“科普讲堂话前沿”“科学文化进基层”部署、推动农业科普资源进校园的具体实践。以青少年为纽带，科研机构与社会公众之间多了一座沟通的桥；通过“请进来”与“走出去”，省农科院在粮食安全、乡村振兴等领域的创新成果被更多年轻人看见。知农、爱农、兴农的种子，正在校园里悄悄发芽。\n\n接下来，省农科院还将继续深化与学校的合作，让更多青少年在沉浸式体验中走近农业、爱上科学，为龙江农业现代化和乡村振兴积蓄青春力量。\n\n![Image 1](https:\u002F\u002Fq0.itc.cn\u002Fq_70\u002Fimages03\u002F20260921\u002Fccb551427fec4af1a347d1e5b521017a.jpeg)\n\n![Image 2](https:\u002F\u002Fq6.itc.cn\u002Fq_70\u002Fimages03\u002F20260921\u002F9f478e5a65a44654acd668ed043bff16.jpeg)\n\n**黑 龙 江 省 农 业 科 学 院 党 宣**\n\n信 息 来 源：科技推广处张唯一\n\n审核：李佳峰\n\n编辑：王红蕾[返回搜狐，查看更多](https:\u002F\u002Fwww.sohu.com\u002F?strategyid=00001 \"点击进入搜狐首页\")","黑龙江省农业科学院","2026-09-19T00:00:00Z","报道",39,{"impact":106,"substance":106,"depth":63,"authority":13,"freshness":107,"relevant":21,"comment":108},8,7,"省级农科院面向中学的科普活动通稿，属全国科普月系列活动，有科普教育价值但信息增量有限，适合作为科普类资讯收录。",[110],{"name":101,"url":98},[26,112,27,113,114],"农业科普","全国科普月","青少年科学素养",[116,117],"黑龙江省农科院 农业科普进校园","萧红中学 农业科普","黑龙江省农科院农业科普进校园-3230","2026-09-23T00:04:30.535981Z",{"id":121,"title":122,"url":123,"summary":124,"summary_zh":125,"content":9,"source_name":126,"source_url":123,"published_at":71,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":127,"score_detail":128,"sources":131,"tags":133,"search_phrases":137,"slug":140,"view_count":35,"doi":141,"paper":142,"created_at":169},3169,"Climate change and the rising threat of Macrophomina phaseolina: implications for food security, food safety, and sustainable crop health management","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10725-026-01525-5","Abstract Macrophomina phaseolina is a destructive soil-borne necrotrophic fungal pathogen that causes substantial yield losses in a wide range of economically important crops worldwide. Disease severity is strongly enhanced under drought, high temperature, and salinity stress, conditions that are becoming increasingly prevalent under current climate change scenarios. This review examines the interactions between climate-driven abiotic stress, host physiological regulation, and pathogen aggressiveness, highlighting how stress-induced disruptions in hormonal signalling, reactive oxygen species homeostasis, antioxidant defence systems, and plant metabolism collectively increase susceptibility to M. phaseolina . Recent advances in understanding pathogen virulence mechanisms, plant immune responses, and resistance-associated molecular pathways are synthesised together with emerging evidence from transcriptomics, proteomics, metabolomics, and comparative genomics. The review further evaluates current management strategies, including host resistance, biological control, plant growth-promoting microorganisms, stress priming, and integrated disease management, while discussing their limitations under field conditions. Emerging technologies such as precision agriculture, remote sensing, artificial intelligence-assisted disease forecasting, and multi-omics approaches are highlighted as promising tools for improving early diagnosis, risk prediction, and climate-resilient disease management. By integrating advances in plant physiology, molecular biology, and sustainable crop protection, this review provides a comprehensive framework for understanding M. phaseolina pathogenesis under changing environmental conditions. It identifies key research priorities to improve crop resilience and safeguard global food security.","摘要 菜豆壳球孢（Macrophomina phaseolina）是一种具有破坏性的土传死体营养型真菌病原菌，在全球范围内对多种具有重要经济价值的作物造成严重产量损失。在干旱、高温和盐胁迫条件下，病害严重程度显著加剧，而这些条件在当前气候变化情景下正变得越来越普遍。本文综述了气候驱动的非生物胁迫、寄主生理调控与病原菌致病力之间的相互作用，重点阐述了胁迫诱导的激素信号传导紊乱、活性氧稳态失衡、抗氧化防御系统受损以及植物代谢改变如何共同增加对菜豆壳球孢的易感性。本文综合了病原菌毒力机制、植物免疫反应及抗性相关分子通路方面的最新研究进展，并结合转录组学、蛋白质组学、代谢组学和比较基因组学的新兴证据。本文进一步评估了当前的管理策略，包括寄主抗性、生物防治、植物促生微生物、胁迫 priming 和病害综合管理，同时讨论了这些策略在田间条件下的局限性。精准农业、遥感、人工智能辅助病害预测和多组学方法等新兴技术被重点介绍为改善早期诊断、风险预测和气候韧性病害管理的有前景的工具。通过整合植物生理学、分子生物学和可持续作物保护方面的进展，本文为理解变化环境条件下菜豆壳球孢的致病机制提供了综合框架，并确定了提高作物韧性和保障全球粮食安全的关键研究优先方向。","Plant Growth Regulation",80,{"impact":17,"substance":129,"depth":17,"authority":19,"freshness":13,"relevant":21,"comment":130},20,"核心期刊综述，系统梳理气候胁迫下土传病害机制与AI遥感等智慧防控手段，对农业信息化与粮食安全主题有聚合价值。",[132],{"name":126,"url":123},[26,134,135,136,27],"粮食安全","植物病害","气候变化",[138,139],"Macrophomina phaseolina 病害 防控","气候变暖 土传病害 粮食安全","Macrophominaphaseolina病害防控-3169","10.1007\u002Fs10725-026-01525-5",{"doi":141,"openalex_id":143,"authors":144,"venue":126,"cited_by_count":35,"oa_url":123,"card":164,"direction":58,"ingested_from":60},"W7213972258",[145,147,149,152,155,158,161],{"name":146,"orcid":9},"Sindiswa Khawula",{"name":148,"orcid":9},"Siyabonga Ntshalitshali",{"name":150,"orcid":151},"Arun Gokul","https:\u002F\u002Forcid.org\u002F0000-0003-1575-0632",{"name":153,"orcid":154},"Lee‐Ann Niekerk","https:\u002F\u002Forcid.org\u002F0000-0002-9788-3131",{"name":156,"orcid":157},"Ashwil Klein","https:\u002F\u002Forcid.org\u002F0000-0002-5606-886X",{"name":159,"orcid":160},"Marshall Keyster","https:\u002F\u002Forcid.org\u002F0000-0002-8718-736X",{"name":162,"orcid":163},"Mbukeni Nkomo","https:\u002F\u002Forcid.org\u002F0000-0002-7652-1588",{"tldr":165,"method":166,"finding":167,"direction":91,"opportunity":168},"综述气候变化下干旱高温盐胁迫加剧菜豆壳球孢菌病害的机制与可持续防控策略。","整合转录组、蛋白组、代谢组、比较基因组及精准农业、遥感、AI预测等技术。","气候胁迫破坏激素与ROS平衡降低作物抗性，需多组学与智能技术实现早期预警和抗性管理。","可构建融合多组学与气象遥感的AI病害预警模型，并研发胁迫 priming 与生防协同的田间方案。","2026-09-22T23:30:22.790623Z",{"id":171,"title":172,"url":173,"summary":174,"summary_zh":175,"content":9,"source_name":176,"source_url":173,"published_at":71,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":177,"score_detail":178,"sources":180,"tags":182,"search_phrases":185,"slug":188,"view_count":35,"doi":189,"paper":190,"created_at":214},3167,"Temporal trend analysis and multi-temporal satellite feature integration for mango orchard acreage estimation using machine learning algorithms approach","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-65828-3","Accurate estimation of horticultural crop acreage is essential for agricultural planning, market forecasting and evidence-based policy formulation. The present study investigated long-term trends in mango cultivation and developed a multi-temporal remote sensing framework for mango orchard acreage estimation in Navsari District using integrated optical, SAR and machine learning approaches. Time-series (temporal) data spanning 23 years (2001–02 to 2023–24) were analyzed using polynomial regression models to evaluate trends in mango area and production. Linear regression best represented area expansion trends (Adj. R² = 0.969), whereas cubic regression better captured production variability (Adj. R² = 0.634), indicating climatic and seasonal influences on productivity. For orchard classification and acreage estimation, multi-temporal Sentinel-2 imagery acquired from November 2023 to March 2024 was processed within a phenology-guided framework. Monthly composites were generated and integrated with Sentinel-1 SAR backscatter data, vegetation indices (NDVI, GNDVI, NDRE, SAVI, EVI and NDMI) and texture metrics derived from Gray Level Co-occurrence Matrix (GLCM) analysis. A comprehensive 72-band feature stack was developed for classification. Four machine learning algorithms, namely Random Forest (RF), XGBoost (XGB), Support Vector Machine (SVM) and Multinomial Logistic Regression (MNLR) were evaluated for orchard discrimination. Among the tested models, RF achieved the highest classification performance with an Overall Accuracy of 99.80% and a Kappa coefficient of 0.997, followed by SVM (99.30%), MNLR (98.21%) and XGB (9.20%). The RF model estimated mango orchard area at 36,099.94 ha, showing the closest agreement with official horticultural statistics (34,363 ha) with only 5.05% estimation error. In contrast, SVM and MNLR overestimated orchard extent by 16.03% and 43.37%, respectively. The proposed framework provides a reliable and scalable methodology for operational horticultural monitoring, crop inventory generation and precision agricultural planning in tropical orchard ecosystems.","准确估算园艺作物种植面积对于农业规划、市场预测和循证政策制定至关重要。本研究探讨了芒果种植的长期趋势，并开发了一个多时相遥感框架，结合光学、合成孔径雷达（SAR）和机器学习方法，用于纳夫萨里县芒果园种植面积估算。利用多项式回归模型分析了跨越23年（2001—02年至2023—24年）的时间序列数据，以评估芒果面积和产量的变化趋势。线性回归最能表征面积扩张趋势（调整R² = 0.969），而三次回归更能捕捉产量变异性（调整R² = 0.634），表明气候和季节性因素对生产力具有影响。在果园分类和面积估算方面，基于物候指导框架处理了2023年11月至2024年3月获取的多时相Sentinel-2影像。生成了月度合成影像，并将其与Sentinel-1 SAR后向散射数据、植被指数（NDVI、GNDVI、NDRE、SAVI、EVI和NDMI）以及基于灰度共生矩阵（GLCM）分析提取的纹理指标进行整合。构建了一个包含72个波段的综合特征集用于分类。评估了四种机器学习算法，即随机森林（RF）、XGBoost（XGB）、支持向量机（SVM）和多项逻辑回归（MNLR），用于果园判别。在测试的模型中，RF取得了最高的分类性能，总体精度为99.80%，Kappa系数为0.997，其次是SVM（99.30%）、MNLR（98.21%）和XGB（9.20%）。RF模型估算的芒果园面积为36,099.94公顷，与官方园艺统计数据（34,363公顷）最为接近，估算误差仅为5.05%。相比之下，SVM和MNLR分别高估了果园面积16.03%和43.37%。所提出的框架为热带果园生态系统中的业务化园艺监测、作物清单生成和精准农业规划提供了一种可靠且可扩展的方法。","Scientific Reports",78,{"impact":19,"substance":18,"depth":17,"authority":19,"freshness":13,"relevant":21,"comment":179},"方法扎实、数据规模大且精度高，但属区域性作物遥感估产研究，产业影响有限，可作为技术方法类精选。",[181],{"name":176,"url":173},[26,79,183,27,184],"芒果","作物估产",[186,187],"Navsari 芒果 遥感估产","Sentinel-2 芒果 果园面积","Navsari芒果遥感估产-3167","10.1038\u002Fs41598-026-65828-3",{"doi":189,"openalex_id":191,"authors":192,"venue":176,"cited_by_count":35,"oa_url":173,"card":209,"direction":58,"ingested_from":60},"W7213920056",[193,195,198,200,202,205,207],{"name":194,"orcid":9},"V. Raju",{"name":196,"orcid":197},"Yogesh A. Garde","https:\u002F\u002Forcid.org\u002F0000-0002-0297-316X",{"name":199,"orcid":9},"Dr. V. S. Thorat",{"name":201,"orcid":9},"V. T. Shinde",{"name":203,"orcid":204},"Nitin Varshney","https:\u002F\u002Forcid.org\u002F0000-0001-9144-5475",{"name":206,"orcid":9},"Alok Shrivastava",{"name":208,"orcid":9},"A. P. Chaudhary",{"tldr":210,"method":211,"finding":212,"direction":58,"opportunity":213},"融合多时相Sentinel-1\u002F2与机器学习，估算印度芒果园面积并分析23年种植趋势。","23年时序回归分析；Sentinel-2月合成+SAR+植被指数+GLCM纹理共","RF精度最高（总体精度99.80%，Kappa 0.997），面积估算误差仅5.05%，优于SVM和","可迁移该多时相SAR-光学特征框架至其他热带果园，并探索深度学习与物候自适应特征优化。","2026-09-22T23:30:22.619289Z",{"id":216,"title":217,"url":218,"summary":219,"summary_zh":9,"content":9,"source_name":220,"source_url":9,"published_at":11,"category":12,"cover_url":9,"hotness":221,"is_selected":14,"score":222,"score_detail":223,"sources":226,"tags":231,"search_phrases":235,"slug":238,"view_count":35,"doi":239,"paper":240,"created_at":262},3122,"Advances in Information Sensing and Intelligent Monitoring of Field Crops Throughout the Full Growth Cycle（大田作物全生育期信息感知与智能监测研究进展）","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F18\u002F1852","江苏大学农业工程学院 Tang Ruifan 等在《Agronomy》16(18): 1852 发表综述（2026-09-20 发表）：大田作物在不同生育阶段持续变化、呈现显著空间异质性、需在短作业窗口内进行管理。研究以生育阶段为主线组织文献，通过\"农业需求—可观测变量—感知平台—数据处理方法—验证设计—状态解释—管理或装备输出\"通用链条分析。从卫星遥感、无人机感知、地面与近端感知、田间物联网、机载传感器、多源融合、作物模型与机器学习方法按空间支撑、时间连续性、尺度匹配、田间稳健性、迁移条件、不确定性与操作适用性比较。综述报告作物表型反演、田间环境表征、生物胁迫识别在特定条件下已建立；跨阶段状态继承、一致参考测量、独立验证、监测结果向可执行任务转化仍不充分。提出生命周期导向的信息处理视角，未来应加强跨作物跨区域验证、机理性与数据驱动模型协同、不确定性报告、互操作性和田间反馈。","MDPI Agronomy",25,76,{"impact":224,"substance":129,"depth":17,"authority":73,"freshness":20,"relevant":21,"comment":225},16,"江苏大学团队在核心期刊发表的综述，系统梳理大田作物全生育期感知与监测技术链条，专业深度与信息增量较高，但属学术综述、产业影响有限，适合进入主题聚合而非头条精选。",[227,228],{"name":220,"url":218},{"name":229,"url":230},"Agronomy","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16181852",[26,232,233,27,234],"农业物联网","作物表型","大田作物",[236,237],"江苏大学 大田作物 智能监测","Agronomy 作物全生育期 信息感知","江苏大学大田作物智能监测-3122","10.3390\u002Fagronomy16181852",{"doi":239,"openalex_id":241,"authors":242,"venue":229,"cited_by_count":35,"oa_url":230,"card":257,"direction":58,"ingested_from":60},"W7213886235",[243,245,248,250,252,254],{"name":244,"orcid":9},"Ruifan Tang",{"name":246,"orcid":247},"Yapeng Wu","https:\u002F\u002Forcid.org\u002F0009-0008-1808-8959",{"name":249,"orcid":9},"Liming Zhang",{"name":251,"orcid":9},"Youqi Xu",{"name":253,"orcid":9},"Yu Zhang",{"name":255,"orcid":256},"Zhong Tang","https:\u002F\u002Forcid.org\u002F0000-0002-2724-115X",{"tldr":258,"method":259,"finding":260,"direction":58,"opportunity":261},"综述大田作物全生育期信息感知与智能监测，按生育阶段梳理技术并指出转化不足。","以生育阶段为主线，比较卫星、无人机、地面物联网、模型与机器学习等方法。","表型反演与胁迫识别已有条件建立，但跨阶段继承、独立验证与可执行转化不足。","可研究跨生育阶段状态继承建模、一致参考测量与监测结果向田间作业指令的转化。","2026-09-22T00:05:38.276747Z",{"id":264,"title":265,"url":266,"summary":267,"summary_zh":268,"content":9,"source_name":269,"source_url":266,"published_at":102,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":270,"score_detail":271,"sources":274,"tags":276,"search_phrases":278,"slug":281,"view_count":35,"doi":282,"paper":283,"created_at":303},3013,"AI and remote sensing for fungal and oomycete disease surveillance: current landscape and biological integration","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs41348-026-01352-w","AI and remote sensing for fungal and oomycete disease surveillance: current landscape and biological integration。Journal of Plant Diseases and Protection","人工智能与遥感在真菌及卵菌病害监测中的应用：现状与生物学整合。《植物病害与保护杂志》","Journal of Plant Diseases and Protection",77,{"impact":17,"substance":129,"depth":272,"authority":73,"freshness":20,"relevant":21,"comment":273},17,"核心期刊综述，系统梳理AI与遥感在真菌及卵菌病害监测中的进展与生物学整合路径，对智慧农业植保方向有参考价值。",[275],{"name":269,"url":266},[26,78,135,27,277],"病害预警",[279,280],"AI 遥感 真菌病害 监测","植物病害 遥感 预警","AI遥感真菌病害监测-3013","10.1007\u002Fs41348-026-01352-w",{"doi":282,"openalex_id":284,"authors":285,"venue":269,"cited_by_count":35,"oa_url":9,"card":298,"direction":58,"ingested_from":60},"W7213649225",[286,288,290,292,295],{"name":287,"orcid":9},"Biju Vadakkemukadiyil Chellappan",{"name":289,"orcid":9},"C. L. Biji",{"name":291,"orcid":9},"Vanshika Arun Meda",{"name":293,"orcid":294},"Sajad Ali","https:\u002F\u002Forcid.org\u002F0000-0002-3230-1436",{"name":296,"orcid":297},"Sherif Mohamed El‐Ganainy","https:\u002F\u002Forcid.org\u002F0000-0001-5226-4604",{"tldr":299,"method":300,"finding":301,"direction":58,"opportunity":302},"综述AI与遥感在真菌及卵菌病害监测中的现状，强调生物信息整合。","文献综述，整合AI、遥感与病原生物学数据。","AI与遥感结合可提升病害监测，但需融入病原生物学机制。","可研究将病原生活史与遥感时序特征耦合的病害预警模型。","2026-09-20T23:30:21.177583Z"]