[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3169":3,"related-3169":67},{"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":22,"tags":24,"search_phrases":30,"slug":33,"view_count":34,"doi":35,"paper":36,"created_at":66},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。",null,"Plant Growth Regulation","2026-09-22T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":13,"relevant":20,"comment":21},18,20,14,1,"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。",[23],{"name":10,"url":6},[25,26,27,28,29],"智慧农业","粮食安全","植物病害","气候变化","遥感监测",[31,32],"Macrophomina phaseolina 病害 防控","气候变暖 土传病害 粮食安全","Macrophominaphaseolina病害防控-3169",0,"10.1007\u002Fs10725-026-01525-5",{"doi":35,"openalex_id":37,"authors":38,"venue":10,"cited_by_count":34,"oa_url":6,"card":58,"direction":64,"ingested_from":65},"W7213972258",[39,41,43,46,49,52,55],{"name":40,"orcid":9},"Sindiswa Khawula",{"name":42,"orcid":9},"Siyabonga Ntshalitshali",{"name":44,"orcid":45},"Arun Gokul","https:\u002F\u002Forcid.org\u002F0000-0003-1575-0632",{"name":47,"orcid":48},"Lee‐Ann Niekerk","https:\u002F\u002Forcid.org\u002F0000-0002-9788-3131",{"name":50,"orcid":51},"Ashwil Klein","https:\u002F\u002Forcid.org\u002F0000-0002-5606-886X",{"name":53,"orcid":54},"Marshall Keyster","https:\u002F\u002Forcid.org\u002F0000-0002-8718-736X",{"name":56,"orcid":57},"Mbukeni Nkomo","https:\u002F\u002Forcid.org\u002F0000-0002-7652-1588",{"tldr":59,"method":60,"finding":61,"direction":62,"opportunity":63},"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。","农业人工智能与决策模型","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。","农业遥感与作物表型","openalex","2026-09-22T23:30:22.790623Z",{"total":68,"page":20,"page_size":68,"items":69},6,[70,107,149,189,243,270],{"id":71,"title":72,"url":73,"summary":74,"summary_zh":75,"content":9,"source_name":76,"source_url":73,"published_at":77,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":78,"score_detail":79,"sources":84,"tags":86,"search_phrases":89,"slug":92,"view_count":34,"doi":93,"paper":94,"created_at":106},2040,"Climate Change and Agricultural Insect Pests: Ecological Mechanisms, Crop Productivity Impacts and Climate Adaptation Strategies","https:\u002F\u002Fdoi.org\u002F10.47495\u002Fokufbed.2000761","Climate change has emerged as one of the most significant challenges affecting agricultural production systems worldwide. Rising temperatures, altered precipitation patterns, increasing atmospheric carbon dioxide concentrations, and the growing frequency of extreme weather events are substantially influencing the biology, ecology, distribution, and population dynamics of agricultural pests. These changes contribute to increased pest abundance, expanded geographical ranges, higher overwintering success, accelerated development rates, and greater numbers of generations per year. Consequently, pest-induced crop losses are expected to increase, posing serious threats to crop productivity, agricultural sustainability, and global food security. In addition to direct effects on pest populations, climate change disrupts plant–pest–natural enemy interactions and weakens biological control mechanisms, further increasing pest pressure within agricultural ecosystems. This review examines the effects of climate change on agricultural insect pest populations and evaluates their implications for crop productivity. Particular attention is given to temperature increases, changes in precipitation and humidity regimes, geographical distribution shifts, invasive species expansion, and phenological mismatches. Furthermore, innovative adaptation strategies including integrated pest management, artificial intelligence-based forecasting systems, precision agriculture technologies, climate-smart agriculture approaches, and remote sensing applications are discussed as potential tools for enhancing agricultural resilience under changing climatic conditions. The findings indicate that sustainable management of climate-related pest risks requires multidisciplinary approaches integrating climate information, pest monitoring, ecological processes, and advanced decision-support technologies. Developing climate-resilient, technology-supported and ecologically based pest management strategies will be essential for safeguarding agricultural productivity and long-term global food security.","气候变化已成为影响全球农业生产系统的最重大挑战之一。气温上升、降水模式改变、大气二氧化碳浓度增加以及极端天气事件日益频繁，正在显著影响农业害虫的生物学、生态学、分布和种群动态。这些变化导致害虫丰度增加、地理分布范围扩大、越冬成功率提高、发育速率加快以及每年世代数增多。因此，害虫引起的作物损失预计将增加，对作物生产力、农业可持续性和全球粮食安全构成严重威胁。除对害虫种群的直接影响外，气候变化还扰乱了植物—害虫—天敌之间的相互作用，削弱了生物防治机制，进一步加剧了农业生态系统内的害虫压力。本文综述了气候变化对农业害虫种群的影响，并评估了其对作物生产力的意义。特别关注了温度升高、降水和湿度状况变化、地理分布转移、入侵物种扩散以及物候错配等方面。此外，还讨论了创新性适应策略，包括有害生物综合治理、基于人工智能的预测系统、精准农业技术、气候智慧型农业方法以及遥感应用，作为在气候变化条件下增强农业韧性的潜在工具。研究结果表明，气候相关害虫风险的可持续管理需要多学科方法，整合气候信息、害虫监测、生态过程和先进决策支持技术。开发气候韧性、技术支撑和基于生态的害虫管理策略，对于保障农业生产力和长期全球粮食安全至关重要。","Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi","2026-09-09T00:00:00Z",76,{"impact":17,"substance":18,"depth":80,"authority":81,"freshness":82,"relevant":20,"comment":83},17,12,9,"系统综述气候变化对农业害虫生态机制与作物生产力的影响，并整合IPM、AI预测、精准农业与遥感等适应策略，对智慧植保与气候韧性农业有参考价值。",[85],{"name":76,"url":73},[25,87,26,88,28,29],"农业人工智能","病虫害防控",[90,91],"农业人工智能 病虫害防控 智慧农业 气候变化","农业人工智能 病虫害防控","农业人工智能病虫害防控智慧农业气候变化-2040","10.47495\u002Fokufbed.2000761",{"doi":93,"openalex_id":95,"authors":96,"venue":76,"cited_by_count":34,"oa_url":73,"card":100,"direction":105,"ingested_from":65},"W7212022123",[97],{"name":98,"orcid":99},"Ekrem ASLAN","https:\u002F\u002Forcid.org\u002F0000-0001-8829-7301",{"tldr":101,"method":102,"finding":103,"direction":62,"opportunity":104},"综述气候变化对农业害虫生态机制、作物生产力影响及气候适应策略。","文献综述，整合气候数据、害虫监测与AI预测、遥感等技术。","气候变暖扩大害虫分布、增加世代与危害，削弱生物防治，威胁粮食安全。","可构建融合气候、遥感与AI的害虫风险预警决策模型，填补多尺度动态预测空白。","智慧农业 \u002F 农业物联网","2026-09-10T23:30:09.295781Z",{"id":108,"title":109,"url":110,"summary":111,"summary_zh":112,"content":9,"source_name":113,"source_url":110,"published_at":114,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":115,"score_detail":116,"sources":119,"tags":121,"search_phrases":123,"slug":126,"view_count":34,"doi":127,"paper":128,"created_at":148},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","2026-09-19T00:00:00Z",77,{"impact":17,"substance":18,"depth":80,"authority":117,"freshness":82,"relevant":20,"comment":118},13,"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。",[120],{"name":113,"url":110},[25,87,27,29,122],"病害预警",[124,125],"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":127,"openalex_id":129,"authors":130,"venue":113,"cited_by_count":34,"oa_url":9,"card":143,"direction":64,"ingested_from":65},"W7213649225",[131,133,135,137,140],{"name":132,"orcid":9},"Biju Vadakkemukadiyil Chellappan",{"name":134,"orcid":9},"C. L. Biji",{"name":136,"orcid":9},"Vanshika Arun Meda",{"name":138,"orcid":139},"Sajad Ali","https:\u002F\u002Forcid.org\u002F0000-0002-3230-1436",{"name":141,"orcid":142},"Sherif Mohamed El‐Ganainy","https:\u002F\u002Forcid.org\u002F0000-0001-5226-4604",{"tldr":144,"method":145,"finding":146,"direction":64,"opportunity":147},"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",{"id":150,"title":151,"url":152,"summary":153,"summary_zh":154,"content":9,"source_name":155,"source_url":152,"published_at":156,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":78,"score_detail":157,"sources":160,"tags":162,"search_phrases":165,"slug":168,"view_count":34,"doi":169,"paper":170,"created_at":188},2769,"Climate change impacts and resilience pathways in somalia through a systematic review","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs42452-026-09538-5","Somalia is among the most climate-vulnerable countries worldwide, characterized by predominantly arid and semi-arid regions increasingly affected by recurrent droughts, irregular precipitation, rising temperatures, land degradation, and water scarcity, all of which compromise livelihoods, food security, and socio-economic stability. This study addresses the limited synthesis of recent, spatially explicit evidence on climate change impacts in Somalia by bringing together current literature and institutional reports to analyze the effects of climate change and identify adaptation and resilience strategies The review is entirely desk-based and utilizes 30 secondary data articles from reputable sources such as FAO-SWALIM, the World Bank, ICPAC, FSNAU, USAID, and peer-reviewed research articles from Scopus and web of science published from 2008 to 2025. The results showed that over 60% of Somalia’s territory is categorized as arid or hyper-arid, with the southern parts seeing the most acute soil erosion and environmental deterioration. Significant drought occurrences, especially in 2010–2011 and 2016–2017, were greatly impacted by extensive climatic factors including ENSO and the Indian Ocean Dipole. Climate variability has resulted in reduced crop yields, pasture degradation, livestock mortality, persistent water shortages, and extensive food insecurity, while inadequate governance, restricted climate financing, and deficient early-warning and monitoring systems hinder effective adaptation. The review emphasizes that climate change presents a systemic threat to Somalia’s environmental and livelihood systems, highlighting the necessity for integrated, spatially informed strategies that incorporate GIS-based environmental monitoring, sustainable land and water management, climate-smart agriculture, strengthened institutions, and enhanced access to climate finance to support long-term resilience and sustainable development.","索马里是全球最易受气候变化影响的国家之一，其国土以干旱和半干旱地区为主，日益受到反复出现的干旱、降水不规律、气温上升、土地退化及水资源短缺的影响，这些问题共同威胁着生计、粮食安全和社会经济稳定。本研究针对索马里气候变化影响近期空间明确证据综合不足的问题，汇集当前文献和机构报告，分析气候变化的影响并识别适应与韧性策略。本综述完全基于案头研究，使用了来自FAO-SWALIM、世界银行、ICPAC、FSNAU、USAID等知名来源的30篇二手数据文献，以及2008年至2025年间发表于Scopus和Web of Science的同行评审研究论文。结果表明，索马里超过60%的领土被归类为干旱或极度干旱，南部地区土壤侵蚀和环境退化最为严重。重大干旱事件，尤其是2010—2011年和2016—2017年的干旱，受到包括厄尔尼诺-南方涛动（ENSO）和印度洋偶极子（IOD）在内的广泛气候因素的显著影响。气候变率导致作物减产、牧场退化、牲畜死亡、持续的水资源短缺和大范围的粮食不安全，而治理不足、气候融资受限以及预警和监测系统薄弱则阻碍了有效适应。本综述强调，气候变化对索马里的环境和生计系统构成系统性威胁，凸显了采取综合性、空间信息化策略的必要性，这些策略应包括基于GIS的环境监测、可持续土地和水资源管理、气候智慧型农业、强化制度以及增强气候融资渠道，以支持长期韧性和可持续发展。","Discover Applied Sciences","2026-09-16T00:00:00Z",{"impact":17,"substance":18,"depth":80,"authority":117,"freshness":158,"relevant":20,"comment":159},8,"系统综述整合2008-2025年30篇文献，揭示索马里干旱与粮食安全影响并提出GIS监测与气候智慧农业等韧性路径，对干旱区农业信息化有参考价值。",[161],{"name":155,"url":152},[26,28,163,29,164],"气候智慧农业","干旱监测",[166,167],"气候智慧农业 干旱监测 气候变化 粮食安全","气候智慧农业 干旱监测","气候智慧农业干旱监测气候变化粮食安全-2769","10.1007\u002Fs42452-026-09538-5",{"doi":169,"openalex_id":171,"authors":172,"venue":155,"cited_by_count":34,"oa_url":152,"card":182,"direction":105,"ingested_from":65},"W7213257664",[173,175,177,180],{"name":174,"orcid":9},"Abdiaziz Hassan Nur",{"name":176,"orcid":9},"Mohamed Osman Abdulkadir",{"name":178,"orcid":179},"Omar Ali","https:\u002F\u002Forcid.org\u002F0009-0001-3009-1850",{"name":181,"orcid":9},"Ahmed Sodal Asir",{"tldr":183,"method":184,"finding":185,"direction":186,"opportunity":187},"系统综述索马里气候变化影响与韧性路径，基于30篇文献与机构报告。","文献综述，采用FAO-SWALIM、世行等2008-2025年30篇二手数据。","超60%国土干旱，干旱致减产、缺水与粮食不安全，治理与预警不足阻碍适应。","农业绿色发展与碳","可结合GIS与遥感构建索马里干旱预警和气候智慧型农业适应决策模型。","2026-09-17T23:30:10.557669Z",{"id":190,"title":191,"url":192,"summary":193,"summary_zh":194,"content":9,"source_name":195,"source_url":192,"published_at":196,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":197,"score_detail":198,"sources":203,"tags":205,"search_phrases":207,"slug":210,"view_count":34,"doi":211,"paper":212,"created_at":242},2670,"A knowledge-guided machine learning framework for cross-scale wheat harvest monitoring via sample augmentation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.rse.2026.115671","Accurate monitoring of wheat harvest is crucial for precision agriculture and ensuring food security. However, rapid changes in land surface composition during the harvest period in intensive agricultural regions make it difficult to obtain sufficiently high-confidence ground samples, limiting the performance and generalization of data-driven remote sensing methods. Therefore, this study proposes a Knowledge-Guided Machine Learning (KGML) framework that integrates multi-satellite Earth observation data (PlanetScope, Sentinel-2, and MODIS) to monitor harvest from the field to regional scales. Ground data were collected using vehicle-mounted cameras and smartphones during the 2023 and 2024 wheat harvest periods. The results showed that combining spectral knowledge rules with a Random Forest model (regional accuracy >0.80) generated numerous high-confidence augmented samples from PlanetScope imagery. The augmented dataset was used to train a Hybrid CNN-Transformer-LSTM (HCTL) model with two pathways: Sentinel-2 classification for field-level harvest mapping (overall accuracy = 0.93) and MODIS regression for sub-pixel harvest fraction estimation, which showed high agreement with PlanetScope-derived harvest fractions (R 2 = 0.97, RMSE = 0.07, rRMSE = 0.15). The harvest dates derived from the MODIS harvest fraction time series showed high consistency with field observations (R 2 = 0.82, RMSE = 1.30 days). This framework provides an effective solution for wheat harvest monitoring by bridging the gap between limited ground-truth data and multi-scale satellite observations, thereby supporting food security assessments and informed agricultural management decisions.","准确监测小麦收获对精准农业和保障粮食安全至关重要。然而，在集约化农业区域，收获期地表组成的快速变化使得获取足够高置信度的地面样本变得困难，限制了数据驱动遥感方法的性能和泛化能力。因此，本研究提出了一种知识引导机器学习（KGML）框架，集成多卫星地球观测数据（PlanetScope、Sentinel-2和MODIS），实现从田块到区域尺度的收获监测。地面数据通过车载摄像头和智能手机在2023年和2024年小麦收获期采集。结果表明，将光谱知识规则与随机森林模型相结合（区域精度>0.80），可从PlanetScope影像中生成大量高置信度增强样本。利用该增强数据集训练了混合CNN-Transformer-LSTM（HCTL）模型，该模型包含两条路径：Sentinel-2分类用于田块尺度收获制图（总体精度=0.93），MODIS回归用于亚像元收获比例估算，其结果与PlanetScope-derived收获比例高度一致（R²=0.97，RMSE=0.07，rRMSE=0.15）。由MODIS收获比例时间序列提取的收获日期与田间观测结果高度一致（R²=0.82，RMSE=1.30天）。该框架通过弥合有限地面真值数据与多尺度卫星观测之间的差距，为小麦收获监测提供了有效解决方案，从而支持粮食安全评估和农业管理决策。","Remote Sensing of Environment","2026-09-15T00:00:00Z",87,{"impact":199,"substance":200,"depth":17,"authority":201,"freshness":82,"relevant":20,"comment":202},22,23,15,"提出知识引导机器学习框架，融合多源卫星数据实现田块到区域尺度的跨尺度小麦收获监测，方法新颖、精度可靠，对精准农业与粮食安全评估有实质参考价值。",[204],{"name":195,"url":192},[25,87,26,29,206],"小麦收获",[208,209],"农业人工智能 小麦收获 智慧农业 粮食安全","农业人工智能 小麦收获","农业人工智能小麦收获智慧农业粮食安全-2670","10.1016\u002Fj.rse.2026.115671",{"doi":211,"openalex_id":213,"authors":214,"venue":195,"cited_by_count":34,"oa_url":192,"card":237,"direction":64,"ingested_from":65},"W7213296259",[215,218,220,222,224,226,228,230,233,235],{"name":216,"orcid":217},"Mingchao Shao","https:\u002F\u002Forcid.org\u002F0000-0003-2619-4272",{"name":219,"orcid":9},"Chongya Jiang",{"name":221,"orcid":9},"Jingwei An",{"name":223,"orcid":9},"Haokai Zhu",{"name":225,"orcid":9},"Yue Li",{"name":227,"orcid":9},"Xia Yao",{"name":229,"orcid":9},"Tao Cheng",{"name":231,"orcid":232},"Hengbiao Zheng","https:\u002F\u002Forcid.org\u002F0009-0008-4778-0450",{"name":234,"orcid":9},"Weixing Cao",{"name":236,"orcid":9},"Yan Zhu",{"tldr":238,"method":239,"finding":240,"direction":64,"opportunity":241},"提出知识引导机器学习框架，用样本增强实现田块到区域尺度的冬小麦收获监测。","融合PlanetScope、Sentinel-2、MODIS与车载相机地面数据，","增强样本训练的HCTL模型田块分类精度0.93，区域收获比例R²=0.97，收获日期误差约1.3天。","可迁移至其他作物收获监测，并探索知识规则自动化构建与跨区域泛化能力。","2026-09-16T23:30:30.474537Z",{"id":244,"title":245,"url":246,"summary":247,"summary_zh":9,"content":9,"source_name":248,"source_url":9,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":249,"score_detail":250,"sources":252,"tags":254,"search_phrases":257,"slug":260,"view_count":34,"doi":9,"paper":261,"created_at":269},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",81,{"impact":17,"substance":199,"depth":17,"authority":117,"freshness":13,"relevant":20,"comment":251},"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。",[253],{"name":248,"url":246},[25,87,255,256,29],"机器学习","作物推荐",[258,259],"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":262,"venue":9,"cited_by_count":34,"oa_url":9,"card":263,"direction":62,"ingested_from":268},[],{"tldr":264,"method":265,"finding":266,"direction":62,"opportunity":267},"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。","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":271,"title":272,"url":273,"summary":274,"summary_zh":9,"content":275,"source_name":276,"source_url":9,"published_at":114,"category":277,"cover_url":9,"hotness":13,"is_selected":14,"score":278,"score_detail":279,"sources":282,"tags":284,"search_phrases":288,"slug":291,"view_count":34,"doi":9,"paper":9,"created_at":292},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","报道",39,{"impact":158,"substance":158,"depth":68,"authority":13,"freshness":280,"relevant":20,"comment":281},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。",[283],{"name":276,"url":273},[25,285,29,286,287],"农业科普","全国科普月","青少年科学素养",[289,290],"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"]