[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5069":3,"related-5069":109},{"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":24,"tags":29,"primary_topic":35,"secondary_topics":41,"classification":42,"search_phrases":81,"slug":84,"view_count":85,"doi":86,"paper":87,"created_at":108},5069,"The impact of digital technology on green economies","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1787960","Introduction Digital agriculture is regarded as an important instrument for achieving green economies, with the potential to raise productivity while reducing environmental impacts. This study explores how digitalization in agriculture affects greenhouse gas emissions, pesticide use and productivity. Methods The research uses a mixed-methods design. Quantitative analysis draws on international environmental and economic indicators for approximately 180 countries between 2000 and 2023 (415 country-year observations), combining Spearman correlations, regression models with GDP interaction terms, and K-means cluster analysis. A qualitative case study of digital agriculture in Latin America complements the statistical findings. Results Digital infrastructure is strongly associated with the adoption of agricultural digital technologies, and smart farming investment is associated with better economic performance of the agricultural sector. Investment in digital agriculture shows a positive association with both emissions and pesticide use, while the interaction with GDP is negative and significant. Cluster analysis confirms that countries with higher GDP and stronger digital infrastructure achieve more balanced environmental outcomes. Discussion The environmental benefits of digital agriculture are conditional on economic capacity. In economically advanced settings, digital technologies reduce resource use, waste and emissions; in economically constrained regions, higher output can offset the environmental gains. Digital agricultural transformation therefore offers opportunities for sustainable development, but its success depends on equitable access to digital technologies and on policy support that mitigates the trade-off between productivity and environmental protection.","引言 数字农业被视为实现绿色经济的重要工具，具有提高生产力同时减少环境影响的潜力。本研究探讨农业数字化如何影响温室气体排放、农药使用和生产力。方法 研究采用混合方法设计。定量分析基于约180个国家2000年至2023年的国际环境和经济指标（415个国家-年份观测值），结合斯皮尔曼相关性分析、包含GDP交互项的回归模型以及K均值聚类分析。拉丁美洲数字农业的定性案例研究补充了统计发现。结果 数字基础设施与农业数字技术的采用密切相关，智慧农业投资与农业部门更好的经济表现相关。数字农业投资与排放量和农药使用量均呈正相关，而与GDP的交互项为负且显著。聚类分析证实，GDP较高且数字基础设施较强的国家实现了更平衡的环境结果。讨论 数字农业的环境效益以经济能力为条件。在经济发达环境中，数字技术减少了资源使用、浪费和排放；在经济受限地区，更高的产出可能抵消环境收益。因此，数字农业转型为可持续发展提供了机遇，但其成功取决于数字技术的公平获取以及能够缓解生产力与环境保护之间权衡的政策支持。",null,"Frontiers in Sustainable Food Systems","2026-10-15T00:00:00Z","论文",25,false,86,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},22,23,19,14,8,1,"基于180国2000—2023年面板数据与拉美案例，揭示数字农业环境效益受经济水平制约，方法扎实、结论有政策参考价值。",[25,26],{"name":10,"url":6},{"name":27,"url":28},"Frontiers in Sustainable Food Systems 2026-10-15","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1787960\u002Ffull",[30,31,32,33,34],"数字农业","智慧农业","农业数字化","农业碳排放","农药减量",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},"F4","crop-protection","农药与绿色防控","\u002Ftopic\u002Finputs-equipment\u002Fcrop-protection","F",[],{"status":43,"locked":14,"version":44,"method":45,"evidence":46},"classified","2026-10-22.2","rules",{"candidates":47,"agricultural_evidence":51},[48,56,63,71,76],{"code":49,"score":50,"supported":51,"title":52,"abstract":53,"tags":55},"I1",12,true,[],[31,30,54],"digital agriculture",[32,30,31],{"code":36,"score":57,"supported":51,"title":58,"abstract":59,"tags":62},7,[],[60,61],"农药","pesticide",[34],{"code":64,"score":65,"supported":51,"title":66,"abstract":67,"tags":70},"B7",6,[],[68,69],"温室","greenhouse",[],{"code":72,"score":22,"supported":14,"title":73,"abstract":74,"tags":75},"H4",[],[],[33],{"code":77,"score":22,"supported":14,"title":78,"abstract":79,"tags":80},"I",[],[],[32],[82,83],"数字农业 温室气体排放","拉丁美洲 数字农业 案例","数字农业温室气体排放-5069",0,"10.3389\u002Ffsufs.2026.1787960",{"doi":86,"openalex_id":88,"authors":89,"venue":10,"cited_by_count":85,"oa_url":99,"card":100,"direction":106,"ingested_from":107},"W7221916400",[90,93,96],{"name":91,"orcid":92},"Botond Géza Kálmán","https:\u002F\u002Forcid.org\u002F0000-0001-8031-8016",{"name":94,"orcid":95},"Zsuzsanna Szeiner","https:\u002F\u002Forcid.org\u002F0000-0003-3051-9674",{"name":97,"orcid":98},"Szilvia Valéria Módosné Szalai","https:\u002F\u002Forcid.org\u002F0009-0005-2164-9014","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1787960\u002Fpdf",{"tldr":101,"method":102,"finding":103,"direction":104,"opportunity":105},"研究约180国2000-2023年数据，分析农业数字化对温室气体排放、农药使用与生产率的影响。","混合方法：Spearman相关、含GDP交互项的回归、K-means聚类，辅以拉","数字农业环境效益取决于经济能力，高GDP国家更均衡，低GDP国家增产抵消环境收益。","农业绿色发展与碳","可探究不同经济水平下数字农业减排阈值与政策干预机制，关注数字鸿沟对绿色转型的调节作用。","智慧农业 \u002F 农业物联网","openalex","2026-10-16T23:30:06.211945Z",{"total":65,"page":22,"page_size":65,"items":110},[111,173,267,350,469,570],{"id":112,"title":113,"url":114,"summary":115,"summary_zh":9,"content":9,"source_name":116,"source_url":9,"published_at":117,"category":12,"cover_url":9,"hotness":118,"is_selected":14,"score":119,"score_detail":120,"sources":124,"tags":126,"primary_topic":130,"secondary_topics":131,"classification":137,"search_phrases":159,"slug":162,"view_count":163,"doi":9,"paper":164,"created_at":172},4648,"[预印本]面向农业机器人在多变天气与地形下基于视觉-语言模型的上下文自适应农药喷洒(Context-Aware Adaptive Pesticide Spraying for Agricultural Robots under Changing Weather and Terrain Using Vision-Language Models)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.08807","arXiv论文(2610.08807,2026年10月6日提交)提出基于视觉-语言模型(VLM)的上下文自适应喷洒框架,通过空间推理整合作物、农药、气象数据驱动喷洒决策,并结合MPPI轨迹跟踪控制。作物行检测精度至少提升30%,机器人可在两种环境中灵活调节喷洒量与行驶速度并减少农药漂移。[预印本未经同行评议,尚未实地部署验证]。","arXiv 2026-10-06 [预印本]","2026-10-06T00:00:00Z",10,72,{"impact":121,"substance":122,"depth":121,"authority":118,"freshness":65,"relevant":22,"comment":123},18,20,"预印本提出VLM驱动的自适应喷洒框架，方法新颖且减药减排指向明确，但未经同行评议与实地验证，宜作前沿动态收录而非重点精选。",[125],{"name":116,"url":114},[31,127,128,129,34],"农业机器人","精准施药","视觉语言模型",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[132],{"code":133,"slug":134,"name":135,"path":136,"parent_code":40},"F6","agricultural-robots","机器人与无人农场","\u002Ftopic\u002Finputs-equipment\u002Fagricultural-robots",{"status":43,"locked":14,"version":44,"method":45,"evidence":138},{"candidates":139,"agricultural_evidence":51},[140,145,150,154],{"code":36,"score":141,"supported":51,"title":142,"abstract":143,"tags":144},21,[60,61],[60],[34,128],{"code":133,"score":146,"supported":51,"title":147,"abstract":148,"tags":149},9,[127],[],[127],{"code":49,"score":22,"supported":14,"title":151,"abstract":152,"tags":153},[],[],[31],{"code":155,"score":22,"supported":14,"title":156,"abstract":157,"tags":158},"I2",[],[],[129],[160,161],"农业机器人 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GIBS卫星遥感与闭环田间记录系统的热带农业AI决策平台为对象，构建覆盖农药化肥生产、田间N2O、灌溉电力与稻田CH4的'摇篮到农场大门'农业碳核算模型。在三个海南代表性场景（芒果园、冬季蔬菜、稻\u002F南繁育种田，按面积40%：30%：30%加权）下，通过蒙特卡洛模拟参数不确定性传播：在完全采纳试点情景下，化学农药使用减少23.5%（90%置信区间15.0%–33.2%），化肥施用减少21.0%（13.8%–28.9%），灌溉水减少16.5%（10.9%–23.5%），碳强度降低21.5%（16.1%–27.2%）。达成化肥减量≥15%与碳强度明确下降的概率分别为90.6%与98.1%，而总节水≥20%的概率仅约20%，表明应采用场景化表述。","arXiv 2609.06737v1 (2026-09)","2026-09-15T00:00:00Z",76,{"impact":121,"substance":17,"depth":121,"authority":50,"freshness":65,"relevant":22,"comment":182},"预印本以蒙特卡洛量化AI平台在海南三类场景的减药减肥与碳减排概率，方法新颖、数据具体，但尚未经同行评审，属细分领域进展。",[184],{"name":178,"url":176},[31,186,34,187,188],"农业人工智能","农业碳核算","海南热带农业",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[],{"status":43,"locked":14,"version":44,"method":45,"evidence":192},{"candidates":193,"agricultural_evidence":51},[194,198,203,210,216,222,228,234,239,245,251],{"code":49,"score":146,"supported":51,"title":195,"abstract":196,"tags":197},[31],[],[31],{"code":36,"score":199,"supported":51,"title":200,"abstract":201,"tags":202},4,[],[60],[34],{"code":204,"score":205,"supported":51,"title":206,"abstract":207,"tags":209},"A1",3,[],[208],"稻田",[],{"code":211,"score":205,"supported":51,"title":212,"abstract":213,"tags":215},"B1",[],[214],"蔬菜",[],{"code":217,"score":205,"supported":51,"title":218,"abstract":219,"tags":221},"B2",[],[220],"芒果",[],{"code":223,"score":205,"supported":51,"title":224,"abstract":225,"tags":227},"H2",[],[226],"土壤",[],{"code":229,"score":205,"supported":51,"title":230,"abstract":231,"tags":233},"H3",[],[232],"灌溉",[],{"code":72,"score":205,"supported":51,"title":235,"abstract":236,"tags":238},[],[237],"农业碳",[],{"code":240,"score":205,"supported":51,"title":241,"abstract":242,"tags":244},"I3",[],[243],"遥感",[],{"code":246,"score":205,"supported":51,"title":247,"abstract":248,"tags":250},"I5",[],[249],"物联网",[],{"code":155,"score":22,"supported":14,"title":252,"abstract":253,"tags":254},[],[],[186],[256,257],"海南 热带农业 AI平台","蒙特卡洛 农业碳核算","海南热带农业AI平台-3651",{"doi":9,"openalex_id":9,"authors":260,"venue":9,"cited_by_count":85,"oa_url":9,"card":261,"direction":104,"ingested_from":171},[],{"tldr":262,"method":263,"finding":264,"direction":104,"opportunity":265},"用蒙特卡洛模拟评估AI智慧农业平台在海南热带农业中的绿色减排效益。","构建摇篮到农场大门碳核算模型，结合蒙特卡洛模拟参数不确定性。","完全采纳下碳强度降21.5%，化肥减量达标概率90.6%，但总节水≥20%概率仅20%。","可延伸研究AI平台在不同作物场景下的节水短板及多目标优化策略。","2026-09-28T00:03:02.301756Z",{"id":268,"title":269,"url":270,"summary":271,"summary_zh":272,"content":9,"source_name":273,"source_url":270,"published_at":274,"category":12,"cover_url":9,"hotness":118,"is_selected":14,"score":180,"score_detail":275,"sources":279,"tags":281,"primary_topic":284,"secondary_topics":285,"classification":290,"search_phrases":331,"slug":334,"view_count":163,"doi":335,"paper":336,"created_at":349},2046,"AI-Powered Crop Protection: Transforming Pest Surveillance and Decision-Making Through Digital Agriculture","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjsrr\u002F2026\u002Fv32i94503","Crop protection is increasingly challenged by changing pest population dynamics, pesticide resistance and the need for more sustainable agricultural production. Artificial intelligence (AI) and digital agriculture offer complementary tools for improving pest surveillance, forecasting and decision-making through automated detection, continuous monitoring and more precise interventions. This review synthesises the applications of machine learning, deep learning, computer vision, Internet of Things-enabled sensing, smart traps, remote sensing, unmanned aerial vehicles, robotics, predictive analytics and intelligent decision support systems in pest management. These approaches support pest identification, population monitoring, outbreak prediction, risk assessment and site-specific management, thereby strengthening the information base required for integrated pest management. The review also examines emerging technologies, including generative AI, foundation models, digital twins, explainable AI, edge intelligence and autonomous agricultural platforms, in relation to future crop protection systems. Despite rapid technological progress, practical deployment remains constrained by limited high-quality datasets, variable field conditions, model generalisation, interoperability, digital infrastructure, cost and farmer adoption. Effective implementation therefore requires transparent, robust and scalable systems that are validated under diverse agricultural conditions and aligned with ecological pest management principles. Overall, integrating AI-enabled surveillance and decision support with integrated pest management can support more timely, adaptive and resource-efficient crop protection while reducing unnecessary interventions.","作物保护正日益受到害虫种群动态变化、农药抗性以及更可持续农业生产需求的挑战。人工智能（AI）与数字农业通过自动检测、持续监测和更精准的干预措施，为改善害虫监测、预测和决策提供了互补性工具。本文综述了机器学习、深度学习、计算机视觉、物联网传感、智能诱捕器、遥感、无人机、机器人技术、预测分析和智能决策支持系统在害虫管理中的应用。这些方法支持害虫识别、种群监测、暴发预测、风险评估和位点特异性管理，从而强化了有害生物综合治理所需的信息基础。本文还探讨了新兴技术，包括生成式人工智能、基础模型、数字孪生、可解释人工智能、边缘智能和自主农业平台，及其在未来作物保护系统中的前景。尽管技术进展迅速，实际部署仍受限于高质量数据集不足、田间条件多变、模型泛化能力、互操作性、数字基础设施、成本以及农民采纳程度等因素。因此，有效实施需要透明、稳健且可扩展的系统，这些系统应在多样化农业条件下得到验证，并与生态害虫管理原则相一致。总体而言，将人工智能驱动的监测与决策支持同有害生物综合治理相结合，可支持更及时、更具适应性和资源效率更高的作物保护，同时减少不必要的干预。","Journal of Scientific Research and Reports","2026-09-09T00:00:00Z",{"impact":121,"substance":122,"depth":276,"authority":277,"freshness":21,"relevant":22,"comment":278},17,13,"系统综述AI与数字农业在病虫害监测预警与决策支持中的应用与瓶颈，信息密度高、结论可靠，对智慧植保方向有参考价值，但属综述类论文而非突破性成果，适合进入精选。",[280],{"name":273,"url":270},[30,31,186,282,283],"病虫害监测","精准植保",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[286],{"code":155,"slug":287,"name":288,"path":289,"parent_code":77},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":43,"locked":14,"version":44,"method":45,"evidence":291},{"candidates":292,"agricultural_evidence":51},[293,298,303,315,320,325],{"code":49,"score":19,"supported":51,"title":294,"abstract":295,"tags":297},[54],[30,296,54],"数字孪生",[30,31],{"code":36,"score":276,"supported":51,"title":299,"abstract":301,"tags":302},[300],"crop protection",[60,61,300],[],{"code":155,"score":118,"supported":51,"title":304,"abstract":305,"tags":314},[],[306,307,308,309,310,311,312,313],"深度学习","机器学习","计算机视觉","machine learning","deep learning","artificial intelligence","computer vision","decision support",[186],{"code":240,"score":65,"supported":51,"title":316,"abstract":317,"tags":319},[],[243,318],"remote sensing",[],{"code":246,"score":65,"supported":51,"title":321,"abstract":322,"tags":324},[],[249,323],"internet of things",[],{"code":326,"score":205,"supported":51,"title":327,"abstract":328,"tags":330},"H8",[],[329],"可持续农业",[],[332,333],"农业人工智能 病虫害监测 数字农业 智慧农业","农业人工智能 病虫害监测","农业人工智能病虫害监测数字农业智慧农业-2046","10.9734\u002Fjsrr\u002F2026\u002Fv32i94503",{"doi":335,"openalex_id":337,"authors":338,"venue":273,"cited_by_count":85,"oa_url":270,"card":343,"direction":106,"ingested_from":107},"W7212081296",[339,341],{"name":340,"orcid":9},"R. Saritha",{"name":342,"orcid":9},"M. Swathi",{"tldr":344,"method":345,"finding":346,"direction":347,"opportunity":348},"综述AI与数字农业在害虫监测、预测及决策支持中的应用与挑战。","综述机器学习、深度学习、计算机视觉、物联网、遥感、无人机与决策支持系统。","AI可提升害虫识别、暴发预测和精准管理，但受数据、泛化、成本与采纳制约。","农业人工智能与决策模型","可探索生成式AI、数字孪生与边缘智能在田间害虫管理中的可解释、可扩展落地验证。","2026-09-10T23:30:09.692422Z",{"id":351,"title":352,"url":353,"summary":354,"summary_zh":355,"content":9,"source_name":356,"source_url":353,"published_at":357,"category":12,"cover_url":9,"hotness":118,"is_selected":14,"score":119,"score_detail":358,"sources":360,"tags":362,"primary_topic":365,"secondary_topics":371,"classification":374,"search_phrases":425,"slug":428,"view_count":205,"doi":429,"paper":430,"created_at":468},1325,"Changing pest dynamics and pesticide use in cotton: Challenges and sustainable management","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.16195","Cotton (Gossypium spp.) is one of the world’s most important fibre crops, yet its sustainability is increasingly challenged by evolving pest complexes, intensified pesticide use, climate change and climate variability. The evidence indicates current knowledge on pest dynamics, pesticide dependence and ecological interactions in cotton agroecosystems, highlighting the shift from bollworm-dominated pest complexes to the increasing prevalence of secondary pests following the widespread adoption of Bacillus thuringiensis (Bt) cotton. Although Bt (Cry toxin)-based transgenic cotton technology initially reduced insecticide use against the bollworm complex, primarily lepidopteran species, the emergence of resistance among bollworms and the upsurge of certain sucking pests in recent decades have reinstated insecticide dependence for pest management. This has led to ecological imbalance, non-target toxicity, the decline of pollinator populations and natural enemy communities, biodiversity loss, environmental contamination, accumulation of residues in products and yield challenges, in addition to increased production costs. The review emphasises the critical role of integrated pest management (IPM) strategies that integrate biological control, habitat diversification, resistant cultivars and threshold-based pesticide applications to restore ecological stability. Alternative approaches such as botanical pesticides, nanoformulations and plant volatile-mediated pest regulation are highlighted as promising tools for sustainable pest control. Advances in precision agriculture, including artificial intelligence (AI), machine learning and unmanned aerial vehicle (UAV)-based pesticide delivery systems, have the potential to improve pest surveillance and reduce chemical inputs. Supply-chain procurement, certification, traceability and consumer demand can encourage lower-pesticide production, although these mechanisms require fair price incentives to avoid transferring compliance costs to smallholders. Sustainable cotton therefore requires region-specific, climate-resilient, evidence-based IPM supported by extension, market incentives, resistance monitoring and coordinated policy.","棉花（Gossypium spp.）是全球最重要的纤维作物之一，然而其可持续性正日益受到害虫群落演变、农药使用强度增加、气候变化及气候变率等因素的挑战。现有证据表明，当前对棉花农业生态系统中害虫动态、农药依赖及生态互作的认识，凸显了自苏云金芽孢杆菌（Bacillus thuringiensis, Bt）棉花广泛推广以来，害虫群落从以棉铃虫为主向次生害虫日益增多的转变。尽管基于Bt（Cry毒素）的转基因棉花技术最初减少了对棉铃虫复合体（主要为鳞翅目害虫）的杀虫剂使用，但近几十年来棉铃虫抗药性的出现及某些刺吸式害虫的激增，重新导致害虫管理对杀虫剂的依赖。这引发了生态失衡、非靶标毒性、传粉昆虫种群及天敌群落减少、生物多样性丧失、环境污染、产品中残留物积累及产量挑战，同时增加了生产成本。本综述强调了综合害虫管理（IPM）策略的关键作用，该策略整合生物防治、生境多样化、抗性品种及基于阈值的农药施用，以恢复生态稳定性。植物源农药、纳米制剂及植物挥发物介导的害虫调控等替代方法，被强调为可持续害虫防治的有前景工具。精准农业的进展，包括人工智能（AI）、机器学习及基于无人机（UAV）的农药施用系统，有望改善害虫监测并减少化学投入。供应链采购、认证、可追溯性及消费者需求可促进低农药生产，但这些机制需辅以公平价格激励，以避免将合规成本转嫁给小农户。因此，可持续棉花生产需要区域特异性、气候韧性、基于证据的IPM，并辅以推广服务、市场激励、抗性监测及协调政策支持。","Plant Science Today","2026-08-31T00:00:00Z",{"impact":121,"substance":17,"depth":121,"authority":50,"freshness":163,"relevant":22,"comment":359},"综述性论文，系统分析棉花害虫动态与农药依赖问题，提出IPM与精准农业等可持续路径，对农业绿色发展有参考价值。",[361],{"name":356,"url":353},[31,186,363,34,364],"棉花","病虫害防治",{"code":366,"slug":367,"name":368,"path":369,"parent_code":370},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[372,373],{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},{"code":155,"slug":287,"name":288,"path":289,"parent_code":77},{"status":43,"locked":14,"version":44,"method":45,"evidence":375},{"candidates":376,"agricultural_evidence":51},[377,382,388,392,400,406,413,419],{"code":36,"score":121,"supported":51,"title":378,"abstract":379,"tags":381},[61],[60,380,61],"生物防治",[34],{"code":366,"score":383,"supported":51,"title":384,"abstract":386,"tags":387},15,[385],"cotton",[363,385],[363],{"code":155,"score":118,"supported":51,"title":389,"abstract":390,"tags":391},[],[307,309,311],[186],{"code":393,"score":146,"supported":51,"title":394,"abstract":395,"tags":399},"H6",[],[396,397,398],"生物多样性","农业生态","biodiversity",[],{"code":49,"score":57,"supported":51,"title":401,"abstract":402,"tags":405},[],[403,404],"精准农业","precision agriculture",[31],{"code":407,"score":65,"supported":51,"title":408,"abstract":409,"tags":412},"H7",[],[410,411],"气候变化","climate change",[],{"code":414,"score":205,"supported":51,"title":415,"abstract":416,"tags":418},"A4",[],[417],"花生",[],{"code":420,"score":205,"supported":51,"title":421,"abstract":422,"tags":424},"G2",[],[423],"小农户",[],[426,427],"农业人工智能 病虫害防治 农药减量 智慧农业","农业人工智能 病虫害防治","农业人工智能病虫害防治农药减量智慧农业-1325","10.14719\u002Fpst.16195",{"doi":429,"openalex_id":431,"authors":432,"venue":356,"cited_by_count":85,"oa_url":353,"card":463,"direction":347,"ingested_from":107},"W7204806269",[433,436,439,442,445,448,451,454,457,460],{"name":434,"orcid":435},"V Veeragowtham","https:\u002F\u002Forcid.org\u002F0009-0002-9822-4901",{"name":437,"orcid":438},"A. Suganthi","https:\u002F\u002Forcid.org\u002F0000-0001-7013-4459",{"name":440,"orcid":441},"M. Murugan","https:\u002F\u002Forcid.org\u002F0000-0002-7485-1153",{"name":443,"orcid":444},"P. Renukadevi","https:\u002F\u002Forcid.org\u002F0000-0001-9665-1681",{"name":446,"orcid":447},"S. Vellaikumar","https:\u002F\u002Forcid.org\u002F0000-0001-9277-457X",{"name":449,"orcid":450},"Ali Iqra","https:\u002F\u002Forcid.org\u002F0009-0008-6095-9471",{"name":452,"orcid":453},"J Kousika","https:\u002F\u002Forcid.org\u002F0000-0002-2455-5268",{"name":455,"orcid":456},"Sheela Venugopal","https:\u002F\u002Forcid.org\u002F0000-0002-4689-4105",{"name":458,"orcid":459},"Prakash Kolanchi","https:\u002F\u002Forcid.org\u002F0000-0003-1244-199X",{"name":461,"orcid":462},"Murugesh Dharani","https:\u002F\u002Forcid.org\u002F0009-0004-6705-1344",{"tldr":464,"method":465,"finding":466,"direction":104,"opportunity":467},"综述棉花害虫动态与农药使用变化，提出可持续管理策略。","综述文献，分析Bt棉影响、害虫抗性及IPM、精准农业等技术。","Bt棉导致次生害虫上升和抗性，需综合IPM与精准农业减少农药。","可研究AI与无人机在棉花害虫监测与精准施药中的实际应用效果及小农采纳机制。","2026-09-01T23:30:34.470501Z",{"id":470,"title":471,"url":472,"summary":473,"summary_zh":474,"content":9,"source_name":475,"source_url":472,"published_at":476,"category":12,"cover_url":9,"hotness":118,"is_selected":14,"score":477,"score_detail":478,"sources":480,"tags":482,"primary_topic":486,"secondary_topics":487,"classification":488,"search_phrases":532,"slug":535,"view_count":85,"doi":536,"paper":537,"created_at":569},5480,"Sustainable Plant Disease Management: Current Advances, Challenges, and Future Directions","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu182010661","Sustainable plant disease management is essential for maintaining agricultural productivity, environmental health, and global food security. Climate change has increased crop vulnerability to emerging and re-emerging plant diseases, posing significant challenges for agricultural sustainability. Although synthetic chemical pesticides have long been effective for disease control, their extensive use is increasingly questioned because of environmental contamination, pathogen resistance, and potential risks to human and ecosystem health. Consequently, plant disease management (PDM) is shifting toward more sustainable and integrated approaches. Future strategies are expected to incorporate multiple disciplines, including: (i) biological control and microbiome engineering to enhance natural plant defense mechanisms; (ii) advanced molecular and genomic technologies, such as CRISPR\u002FCas systems, RNA interference (RNAi), and epigenome editing, to develop durable disease resistance; (iii) smart delivery systems for the precise application of antimicrobial compounds and defense elicitors; (iv) nanotechnology-based platforms for the targeted delivery of nutrients, pesticides, and biocontrol agents, improving efficacy while reducing chemical inputs; (v) digital and precision agriculture technologies, including remote sensing, unmanned aerial vehicles (UAVs), and artificial intelligence (AI)-based diagnostics, to facilitate real-time disease surveillance and targeted interventions; and (vi) agroecological practices such as crop diversification, soil health restoration, and climate-smart farming to enhance agroecosystem resilience. These innovations aim not only to suppress plant pathogens but also to build sustainable, adaptive, and resilient agricultural systems that address future environmental and biological challenges.","可持续的植物病害管理对于维持农业生产力、环境健康和全球粮食安全至关重要。气候变化加剧了作物对新发和再发植物病害的脆弱性，给农业可持续性带来了重大挑战。尽管合成化学农药长期以来在病害防控中行之有效，但其广泛使用正日益受到质疑，原因包括环境污染、病原菌抗药性以及对人类和生态系统健康的潜在风险。因此，植物病害管理（plant disease management, PDM）正转向更加可持续和综合的途径。未来的策略预计将融合多个学科，包括：(i) 生物防治和微生物组工程，以增强植物天然防御机制；(ii) 先进的分子和基因组技术，如CRISPR\u002FCas系统、RNA干扰（RNAi）和表观基因组编辑，以培育持久抗病性；(iii) 用于精准施用抗菌化合物和防御诱导子的智能递送系统；(iv) 基于纳米技术的平台，用于靶向递送养分、农药和生物防治剂，在提高功效的同时减少化学投入；(v) 数字和精准农业技术，包括遥感、无人机（UAVs）和基于人工智能（AI）的诊断，以促进实时病害监测和靶向干预；以及(vi) 农业生态实践，如作物多样化、土壤健康恢复和气候智慧型农业，以增强农业生态系统的韧性。这些创新不仅旨在抑制植物病原菌，还旨在构建可持续、适应性强且具有韧性的农业系统，以应对未来的环境和生物挑战。","Sustainability","2026-10-20T00:00:00Z",81,{"impact":17,"substance":122,"depth":121,"authority":277,"freshness":21,"relevant":22,"comment":479},"系统综述可持续植物病害管理六大技术方向，涵盖AI诊断、纳米递送与基因编辑，对智慧农业与绿色防控具参考价值。",[481],{"name":475,"url":472},[31,403,483,484,380,485],"基因编辑","植物病害","纳米技术",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[],{"status":43,"locked":14,"version":44,"method":45,"evidence":489},{"candidates":490,"agricultural_evidence":51},[491,495,499,506,511,515,519,523,527],{"code":49,"score":21,"supported":51,"title":492,"abstract":493,"tags":494},[],[403,404],[31,403],{"code":36,"score":57,"supported":51,"title":496,"abstract":497,"tags":498},[],[60,380],[380],{"code":500,"score":65,"supported":51,"title":501,"abstract":502,"tags":505},"A8",[],[503,504],"粮食安全","food security",[],{"code":223,"score":65,"supported":51,"title":507,"abstract":508,"tags":510},[],[226,509],"soil",[],{"code":407,"score":65,"supported":51,"title":512,"abstract":513,"tags":514},[],[410,411],[],{"code":240,"score":65,"supported":51,"title":516,"abstract":517,"tags":518},[],[243,318],[],{"code":393,"score":205,"supported":51,"title":520,"abstract":521,"tags":522},[],[397],[],{"code":155,"score":205,"supported":51,"title":524,"abstract":525,"tags":526},[],[311],[],{"code":528,"score":22,"supported":14,"title":529,"abstract":530,"tags":531},"F2",[],[],[483],[533,534],"CRISPR RNAi 作物抗病","基因编辑 智慧农业 植物病害 生物防治","CRISPRRNAi作物抗病-5480","10.3390\u002Fsu182010661",{"doi":536,"openalex_id":538,"authors":539,"venue":475,"cited_by_count":85,"oa_url":472,"card":564,"direction":106,"ingested_from":107},"W7223044776",[540,543,546,549,552,555,558,561],{"name":541,"orcid":542},"Karim M. Hassan","https:\u002F\u002Forcid.org\u002F0000-0002-1241-2307",{"name":544,"orcid":545},"Basma T. Abd-Elhalim","https:\u002F\u002Forcid.org\u002F0000-0002-3009-332X",{"name":547,"orcid":548},"Mohamed K. Abou El-Nasr","https:\u002F\u002Forcid.org\u002F0000-0003-1451-833X",{"name":550,"orcid":551},"Essam Yosef Abdul-Hafeez","https:\u002F\u002Forcid.org\u002F0000-0003-4744-4672",{"name":553,"orcid":554},"Ahmed N. Abdelhamid","https:\u002F\u002Forcid.org\u002F0000-0002-1326-7885",{"name":556,"orcid":557},"Mostafa Abdelkader","https:\u002F\u002Forcid.org\u002F0000-0003-2339-5087",{"name":559,"orcid":560},"Mohamed Abd El-Hamed Nasser","https:\u002F\u002Forcid.org\u002F0000-0002-0328-2969",{"name":562,"orcid":563},"Mahmoud A. A. Ali","https:\u002F\u002Forcid.org\u002F0000-0002-9062-0048",{"tldr":565,"method":566,"finding":567,"direction":106,"opportunity":568},"综述植物病害可持续管理的多学科前沿进展、挑战与未来方向。","文献综述，整合生物防治、基因编辑、纳米递送、数字农业与生态农艺。","未来病害管理需融合生物、分子、纳米与数字技术构建韧性农业系统。","遥感、无人机与AI诊断的实时病害监测闭环仍缺田间验证与低成本落地研究。","2026-10-21T23:30:09.242484Z",{"id":571,"title":572,"url":573,"summary":574,"summary_zh":9,"content":575,"source_name":576,"source_url":9,"published_at":577,"category":578,"cover_url":9,"hotness":118,"is_selected":14,"score":579,"score_detail":580,"sources":582,"tags":584,"primary_topic":588,"secondary_topics":594,"classification":596,"search_phrases":621,"slug":624,"view_count":85,"doi":9,"paper":9,"created_at":625},5281,"省农科院植保所江油武都\"三水\"绿色融合样板田连续10余年综合治理 稻鱼鸭共生","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7696132538393215528","10月12日江油市武都镇阳亭坝金黄稻田迎来丰收,四川省农科院植保所研究员姬红丽蹲在田埂查叶背(无病斑),亩产干谷不低于1300斤;自2013年起省农科院植保所在此实施\"农田有害生物综合治理课题\"持续10余年,通过田埂种植甜荞、蔷薇、紫云英、二月兰、香根草等蜜源植物为天敌提供庇护所,基地稻田麦田天敌种群数量达农户常规田块的10倍以上。","视频加载中...\n\n川观新闻记者 张彧希 四川农村日报记者 徐灿红 洪瑜\n\n10月12日，江油市武都镇阳亭坝，秋风掠过，稻田翻起金色波浪。\n\n四川省农业科学院植物保护研究所研究员姬红丽蹲在田埂上，拔起一株水稻查看叶背——干干净净，没有病斑。“亩产干谷不会低于1300斤。”再过一两天，这里就要开镰。\n\n这片稻田不普通。自2013年起，省农科院植保所在此实施“农田有害生物综合治理课题”，持续10余年。曾经抛荒的土地，如今稻穗丰盈、游鱼戏水、白鹭翩跹，成为国内率先实现水稻、水产、水禽“三水”绿色融合的样板田。\n\n曾经令人头痛的有害生物，是如何被“赶出”这片田的？\n\n![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F0236314001894642bb5bcf371554180b~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1792973320&x-signature=XhegSE7aGrMWXTC4o0zuZt8sDFo%3D)\n俯瞰基地。\n\n**第一招：良种打底，病害止步田外**\n\n“三水”融合，第一关是选品种。“不光看产量和米质，更看重抗病性。”姬红丽说。西南稻区主要病害有稻瘟病、纹枯病、稻曲病，农户常规靠打药防病，但基地全生育期几乎不施化学杀菌剂，底气来自多抗性品种。\n\n连续十多年，团队每年从市场主流品种中筛选多抗性优异品种，在江油基地展示。“江油气候利于多种病害发生，对品种抗性要求更高，也更容易筛出好品种。”姬红丽说。\n\n今年基地集中示范川康优2115、品香优607等优质水稻，在稻瘟、稻曲等病害抗性上表现优异。品种自身抗性强，病害起不来，化学农药自然省了。\n\n品种抗性究竟如何？种子企业也很关心。四川金色绿丹种业有限公司川北区域经理陈胜龙沿试验田看了几遍：“没找到稻瘟病、稻曲病症状，叶片干净，秆壮穗大，产量很乐观。”\n\n**第二招：田埂布阵，天敌与“陷阱”控虫**\n\n![Image 2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002Facfcb249e48cd472f480260e5551bb65~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1792973320&x-signature=B1pojs4Mx2mcaw2GJlWWImuIBDk%3D)\n田埂上种的甜荞。\n\n病害靠品种扛，虫害靠天敌控。不足半米宽的田埂，像一座迷你花园：甜荞开白花，蔷薇正艳，菜粉蝶、蜜蜂穿梭其间。\n\n![Image 3](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F85a45dc6b1fa07d071fd5c3d1578c4f2~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1792973320&x-signature=oCp5UghEu2I%2BkG6F5S36WgQDGUA%3D)\n田埂上种的蔷薇。\n\n“这些都是蜜源植物，是精心选的‘生态卫士’。”姬红丽说。稻麦轮作茬口期，田里没有庄稼，自然天敌没处安身，蜜源植物便补上空当，提供花粉、花蜜和庇护所。\n\n目前田埂还种了紫云英、二月兰，基地稻田麦田里的天敌种群数量，达到农户常规自防田块的10倍以上，全是自然天敌，没有人工投放。\n\n记者发现，田埂上还种着一人高的香根草。姬红丽介绍，这是从泰国引进的“陷阱植物”：气味吸引二化螟产卵，但含有毒活性物质，不适合幼虫发育，孵化幼虫大多低龄死亡，无法发育为成虫。一代代下来，基地二化螟数量越来越低。\n\n效果有数据支撑：水稻螟虫、麦蚜虫连续八年没达到化学防治用药阈值；蜘蛛、青蛙、田螺、泥鳅、黄鳝、蛇、野鸭、翠鸟等指示生物相继回归，并向周边农田扩散。“插秧时，当地村民都来抓黄鳝，知道我们不用药，黄鳝比别处多。”姬红丽笑着说。\n\n**第三招：生态当家，少打药照样高产**\n\n开农资店的罗勇，生意不如以前，但他却为啥挺开心？\n\n罗勇原是当地农资店主，卖农药化肥。“那会儿种植户见虫就打药，我生意也好。”但姬红丽团队来了之后，带来一个新理念：生态当家，少打药照样高产。\n\n“一开始我不信。”姬红丽团队带着罗勇一次次下田示范，请他到基地看效果。“后来发现人家不打药，虫子比我的还少，稻子还壮。”\n\n罗勇动了心，一边开农资店，一边承包土地。如今种植已发展到400多亩。“跟着团队学病虫害防治，品种从他们筛出的抗病品种里选，少打药减少人工，一亩地省100多块钱，产量基本不受影响。”他估计，今年亩产干谷能到1300至1400斤，在武都镇算高的。\n\n自己尝到甜头，罗勇又向周边大户推广：“少打药不是不管，是要靠品种抗病、靠天敌控虫。”如今，武都镇大户基本减少了打药次数，但产量没降。","今日头条川观新闻(2026-10-13)","2026-10-13T00:00:00Z","报道",69,{"impact":121,"substance":122,"depth":276,"authority":118,"freshness":199,"relevant":22,"comment":581},"连续十余年综合治理的实证报道，数据与多方信源扎实，具行业示范价值，但时效偏旧。",[583],{"name":576,"url":573},[585,34,396,586,587],"绿色防控","生态农业","稻鱼鸭共生",{"code":589,"slug":590,"name":591,"path":592,"parent_code":593},"D3","integrated-aquaculture","稻渔种养与生态渔业","\u002Ftopic\u002Faquaculture\u002Fintegrated-aquaculture","D",[595],{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},{"status":43,"locked":14,"version":44,"method":45,"evidence":597},{"candidates":598,"agricultural_evidence":51},[599,604,609,613,617],{"code":36,"score":277,"supported":51,"title":600,"abstract":602,"tags":603},[601],"植保",[601],[34,585],{"code":589,"score":21,"supported":51,"title":605,"abstract":607,"tags":608},[606],"稻鱼",[],[],{"code":204,"score":205,"supported":51,"title":610,"abstract":611,"tags":612},[],[208],[],{"code":326,"score":163,"supported":14,"title":614,"abstract":615,"tags":616},[],[],[586,585],{"code":393,"score":22,"supported":14,"title":618,"abstract":619,"tags":620},[],[],[396],[622,623],"四川省农科院 植保所 江油 稻鱼鸭","三水融合 绿色防控 样板田","四川省农科院植保所江油稻鱼鸭-5281","2026-10-19T00:07:41.376347Z"]