[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3522":3,"related-3522":66},{"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":65},3522,"An agronomically informed framework for temporally transferable yield prediction","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10454-2","Abstract Purpose Yield maps are essential for agricultural decision-making, yet existing methods are often crop- or machine-specific, limiting their broader applicability. Methods This study introduces an agronomically informed framework for temporally transferable yield prediction that combines optimal phenological moment (OPM) selection with a spatial yield redistribution strategy: density-based yield mapping (DBYM). The framework relies on vegetation indices derived from satellite imagery acquired at physiologically meaningful crop stages and on independently known field-scale production values to guide spatial yield allocation. The method was evaluated across 10 growing seasons involving coffee, sugarcane, and wheat under both manual and mechanized production systems in commercial fields located in Brazil and Australia. In addition to DBYM, a simple linear regression baseline using the same OPM-derived inputs was evaluated to investigate the contribution of agronomically informed temporal input selection itself. Results Results demonstrated that OPM-based inputs alone already provided strong predictive capability across seasons, while DBYM improved the spatial coherence of predictions, particularly under sparse calibration scenarios. The index of agreement ranged from 0.73 to 0.94, while Moran’s bivariate index was significant ( p \u003C 0.01) for all DBYM predictions, confirming spatial consistency between observed and predicted yield patterns. RMSE values were comparable to those reported in machine learning and deep learning yield prediction studies, ranging from 0.21 to 0.28 Mg ha⁻¹ for coffee, 5.43–6.22 Mg ha⁻¹ for sugarcane, 0.15–0.26 Mg ha⁻¹ for wheat in Brazil, and 0.44–0.61 Mg ha⁻¹ for wheat in Australia. Conclusion The results suggest that predictive performance can be affected by the agronomic relevance of acquisition timing. By combining agronomically informed temporal inputs with a flexible spatial redistribution framework, DBYM enables the extension of yield map time series across seasons and provides a scalable and resource-efficient alternative for precision agriculture applications in fields lacking dedicated yield monitoring systems.","摘要 目的 产量图对农业决策至关重要，但现有方法往往针对特定作物或机器，限制了其更广泛的适用性。方法 本研究引入了一个基于农学知识的时间可迁移产量预测框架，该框架将最佳物候时刻（OPM）选择与空间产量再分配策略——基于密度的产量制图（DBYM）相结合。该框架依赖于在具有生理意义的作物阶段获取的卫星影像所衍生的植被指数，以及独立已知的田块尺度产量值来指导空间产量分配。该方法在巴西和澳大利亚的商业田块中进行了评估，涵盖10个生长季，涉及咖啡、甘蔗和小麦在人工与机械化生产系统下的种植。除DBYM外，还评估了使用相同OPM衍生输入的简单线性回归基线，以探究基于农学知识的时间输入选择本身的贡献。结果 结果表明，仅基于OPM的输入已在各生长季提供了较强的预测能力，而DBYM改善了预测的空间一致性，尤其是在校准样本稀疏的情景下。一致性指数范围为0.73至0.94，同时所有DBYM预测的Moran双变量指数均显著（p \u003C 0.01），证实了观测产量模式与预测产量模式之间的空间一致性。RMSE值与机器学习和深度学习产量预测研究中报道的值相当，巴西咖啡为0.21至0.28 Mg ha⁻¹，甘蔗为5.43至6.22 Mg ha⁻¹，小麦为0.15至0.26 Mg ha⁻¹，澳大利亚小麦为0.44至0.61 Mg ha⁻¹。结论 结果表明，预测性能可能受到获取时机农学相关性的影响。通过将基于农学知识的时间输入与灵活的空间再分配框架相结合，DBYM能够将产量图时间序列扩展至多个生长季，并为缺乏专用产量监测系统的田块提供了一种可扩展且资源高效的精准农业应用替代方案。",null,"Precision Agriculture","2026-09-25T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,8,1,"提出农学信息驱动的跨季可迁移产量预测框架，多作物多国验证，方法新颖且实用，值得进入每日精选。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","产量预测","精准农业","遥感估产","咖啡",[32,33],"遥感 产量预测 咖啡 甘蔗 小麦","DBYM 产量制图 精准农业","遥感产量预测咖啡甘蔗小麦-3522",0,"10.1007\u002Fs11119-026-10454-2",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":6,"card":57,"direction":63,"ingested_from":64},"W7214356320",[40,43,46,49,51,54],{"name":41,"orcid":42},"Ricardo Canal Filho","https:\u002F\u002Forcid.org\u002F0000-0001-8043-8325",{"name":44,"orcid":45},"José Paulo Molin","https:\u002F\u002Forcid.org\u002F0000-0001-7250-3780",{"name":47,"orcid":48},"Patrick Filippi","https:\u002F\u002Forcid.org\u002F0000-0003-3573-084X",{"name":50,"orcid":9},"Eudocio Rafael Otavio da Silva",{"name":52,"orcid":53},"Luiz Gustavo de Góes Sterle","https:\u002F\u002Forcid.org\u002F0000-0001-9748-3923",{"name":55,"orcid":56},"Marcelo Chan Fu Wei","https:\u002F\u002Forcid.org\u002F0000-0002-8242-8435",{"tldr":58,"method":59,"finding":60,"direction":61,"opportunity":62},"提出农学知情框架，结合最优物候期与密度产量制图，实现跨季节可迁移的产量预测。","利用卫星植被指数与最优物候期选择，结合密度产量制图DBYM，在巴西和澳大利亚多作","仅用最优物候期输入即可跨季节强预测，DBYM提升空间一致性，精度与机器学习方法相当。","农业遥感与作物表型","可探索将农学知情时间选择与深度时空模型结合，提升无产量监测系统田块的跨区域迁移能力。","农业人工智能与决策模型","openalex","2026-09-26T23:30:02.989455Z",{"total":67,"page":21,"page_size":67,"items":68},6,[69,105,141,170,210,255],{"id":70,"title":71,"url":72,"summary":73,"summary_zh":74,"content":9,"source_name":75,"source_url":72,"published_at":76,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":77,"score_detail":78,"sources":83,"tags":85,"search_phrases":88,"slug":91,"view_count":21,"doi":92,"paper":93,"created_at":104},2518,"AI-Driven Transformation in Sustainable Agriculture: A Systematic Review","https:\u002F\u002Fdoi.org\u002F10.18805\u002Fijare.a-6627","The agriculture sector is facing unprecedented pressure from a rapidly growing global population, erratic weather conditions and declining water availability. Traditional farming methods are increasingly proving insufficient in meeting the rising food demand, necessitating a shift towards technology-driven solutions. This paper provides a critical and systematic review of the application of Artificial Intelligence (AI) in agriculture, analysing its impact on productivity and sustainability, while identifying potential barriers hindering adoption. Employing a Systematic Literature Review approach, this study synthesizes recent research organized around the core domains of agricultural production - crop, soil and nutrient, water and irrigation and crop-protection (pest, disease and weed) management, alongside yield forecasting - examining how data-driven tools such as machine learning, the Internet of Things, robotics and computer vision are applied within each and prioritizing studies that reported agronomically meaningful outcomes such as yield gains and resource-use efficiency. The strongest gains identified across the review fall within three agricultural areas - crop and soil monitoring, yield forecasting and robotic field operations such as targeted detection, weeding and harvesting. The technologies enabling them, chiefly machine-learning models such as convolutional neural networks combined with field and remote sensors, have delivered high in-field precision, with reported object-detection precision of up to 95.78% in field conditions. In practical terms, this translates into more efficient use of water, fertilizer and other inputs and into improved yield and crop quality. The review concludes that prioritizing Explainable AI and interoperable farm systems is central to building farmer trust and safeguarding food security under a changing climate.","农业部门正面临着来自全球人口快速增长、天气条件不稳定以及水资源日益减少的前所未有的压力。传统耕作方法在满足不断增长的粮食需求方面日益显得力不从心，因此有必要转向技术驱动的解决方案。本文对人工智能（Artificial Intelligence, AI）在农业中的应用进行了批判性和系统性的综述，分析了其对生产力和可持续性的影响，同时识别了阻碍其采用的潜在障碍。本研究采用系统性文献综述方法，围绕农业生产的核心领域——作物、土壤与养分、水资源与灌溉、作物保护（病虫害和杂草）管理以及产量预测——对近期研究进行了综合梳理，考察了机器学习、物联网、机器人技术和计算机视觉等数据驱动工具在各领域中的应用方式，并优先关注报告了具有农学意义成果（如产量提升和资源利用效率）的研究。综述中发现的最显著收益集中在三个农业领域——作物与土壤监测、产量预测以及机器人田间作业（如靶向检测、除草和收获）。实现这些收益的技术，主要是卷积神经网络等机器学习模型与田间及遥感传感器的结合，已在田间条件下实现了高精度，所报告的目标检测精度在田间条件下高达95.78%。在实际应用中，这意味着水、肥料及其他投入品的更高效利用，以及产量和作物品质的提升。综述得出结论：优先发展可解释人工智能（Explainable AI）和可互操作的农场系统，对于在气候变化背景下建立农民信任和保障粮食安全至关重要。","Indian Journal of Agricultural Research","2026-09-14T00:00:00Z",76,{"impact":17,"substance":79,"depth":80,"authority":81,"freshness":20,"relevant":21,"comment":82},20,17,13,"系统综述梳理AI在作物土壤监测、产量预测与机器人田间作业中的应用成效与推广障碍，结论扎实，对智慧农业方向有参考价值。",[84],{"name":75,"url":72},[26,86,27,87,28],"农业人工智能","可解释AI",[89,90],"农业人工智能 产量预测 智慧农业 精准农业","农业人工智能 产量预测","农业人工智能产量预测智慧农业精准农业-2518","10.18805\u002Fijare.a-6627",{"doi":92,"openalex_id":94,"authors":95,"venue":75,"cited_by_count":35,"oa_url":72,"card":98,"direction":103,"ingested_from":64},"W7212616413",[96],{"name":97,"orcid":9},"Manish Maan",{"tldr":99,"method":100,"finding":101,"direction":63,"opportunity":102},"系统综述AI在可持续农业中的应用，分析生产力与可持续性影响及推广障碍。","系统文献综述，围绕作物、土壤、水、植保和产量预测梳理ML、IoT、机器人、计算机","作物与土壤监测、产量预测和机器人田间作业收益最大，检测精度达95.78%，需可解释AI与互操作系统。","可解释AI与互操作农场系统在农户信任和气候适应中的作用尚缺实证，是值得深入的研究空白。","智慧农业 \u002F 农业物联网","2026-09-15T23:30:13.705421Z",{"id":106,"title":107,"url":108,"summary":109,"summary_zh":110,"content":9,"source_name":111,"source_url":108,"published_at":112,"category":12,"cover_url":9,"hotness":113,"is_selected":14,"score":114,"score_detail":115,"sources":120,"tags":124,"search_phrases":126,"slug":128,"view_count":35,"doi":129,"paper":130,"created_at":140},2337,"High-Precision Crop Yield Prediction Model Combining GANs and Random Forest","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22723504","Abstract Crop yield prediction algorithms are now much more accurate and useful thanks to recent developments in deep learning. In order to analyse crop yield, this study explores the combination of Random Forest methods with Generative Adversarial Networks (GANs). In order to overcome data scarcity and class imbalance, GANs are used for data augmentation, producing realistic synthetic samples that strengthen deep learning models. Various agro-climatic and soil datasets are used to forecast yield using Random Forest, an ensemble machine learning technique. According to comparative analyses, Random Forest outperforms conventional regression models and has great generalization across areas and crops4568, regularly achieving high predictive accuracy (R2 > 0.95). A potent foundation for precision agriculture is provided by the complementary application of Random Forest for prediction and GANs for data enrichment, allowing better precision in crop yield analysis. With proper hyperparameter tuning, RF can achieve very high accuracy (R² up to 0.99 in some studies), making it a preferred choice for practical yield forecasting.","摘要 得益于深度学习的最新发展，作物产量预测算法如今已更加准确和实用。为了分析作物产量，本研究探索了随机森林方法与生成对抗网络（Generative Adversarial Networks，GANs）的结合。为了克服数据稀缺和类别不平衡问题，研究使用GANs进行数据增强，生成逼真的合成样本以强化深度学习模型。研究采用多种农业气候和土壤数据集，利用随机森林这一集成机器学习技术来预测产量。比较分析表明，随机森林优于传统回归模型，并在不同地区和作物间展现出强大的泛化能力，通常能够实现较高的预测精度（R2 > 0.95）。随机森林用于预测与GANs用于数据增强的互补应用，为精准农业提供了有力的基础，使作物产量分析能够实现更高的精度。通过适当的超参数调优，随机森林可以达到非常高的精度（在某些研究中R²高达0.99），使其成为实际产量预测的首选方法。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-30T00:00:00Z",25,61,{"impact":116,"substance":17,"depth":117,"authority":118,"freshness":35,"relevant":21,"comment":119},16,15,12,"将GAN数据增强与随机森林结合用于作物产量预测，方法组合有新意且精度结论明确，但属预印本平台论文、发布日期异常且时效性差，暂不宜进入每日精选。",[121,122],{"name":111,"url":108},{"name":111,"url":123},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22723505",[26,86,27,28,125],"数据增强",[127,90],"农业人工智能 产量预测 数据增强 智慧农业","农业人工智能产量预测数据增强智慧农业-2337","10.5281\u002Fzenodo.22723504",{"doi":129,"openalex_id":131,"authors":132,"venue":111,"cited_by_count":35,"oa_url":108,"card":135,"direction":63,"ingested_from":64},"W7212353714",[133],{"name":134,"orcid":9},"S. Kavitha",{"tldr":136,"method":137,"finding":138,"direction":63,"opportunity":139},"结合GAN数据增强与随机森林，构建高精度作物产量预测模型。","GAN生成合成样本缓解数据稀缺，随机森林基于农业气候与土壤数据预测。","随机森林预测精度高（R²>0.95，部分达0.99），优于传统回归模型。","可探索GAN生成样本的农学合理性验证及跨区域迁移学习以提升泛化能力。","2026-09-13T23:30:43.223973Z",{"id":142,"title":143,"url":144,"summary":145,"summary_zh":9,"content":9,"source_name":146,"source_url":9,"published_at":147,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":148,"score_detail":149,"sources":151,"tags":153,"search_phrases":157,"slug":160,"view_count":35,"doi":9,"paper":161,"created_at":169},3600,"Development of an Adaptive Sprayer Control System for UAV-Based Spot Spraying in Soybean（基于植被覆盖率的自适应无人机大豆点喷控制系统研发）","https:\u002F\u002Fwww.ebiotrade.com\u002Fnewsf\u002F2026-9\u002F20260924002257098.htm","本研究开发了一套集成无人机的自适应喷雾控制系统，将冠层感知与基于阈值的点喷技术相连接。研究人员利用大豆单独覆盖度作为局部杂草斑块检测的基准基线，室内与田间估算结果的R²值分别为0.91和0.69。在播种后24天，采用25%的阈值可将潜在误喷激活率限制在4.8%。室内测试中，自动喷雾执行准确率达到97.3%。研究指出杂草可导致大豆超过30%的产量损失，全球年经济损失达330亿美元。无人机喷洒避免了与土壤的直接接触，减少了土壤压实和作物损伤的风险，提高了关键除草窗口期的操作灵活性。","Smart Agricultural Technology 7.1 2026-09-24","2026-09-24T00:00:00Z",79,{"impact":17,"substance":18,"depth":17,"authority":81,"freshness":20,"relevant":21,"comment":150},"该研究提出基于冠层覆盖度的无人机自适应点喷控制方法，数据详实、结论可靠，对精准植保技术研发具有参考价值，值得进入每日精选。",[152],{"name":146,"url":144},[26,28,154,155,156],"大豆","无人机植保","杂草管理",[158,159],"无人机 大豆 点喷","自适应喷雾 大豆 杂草","无人机大豆点喷-3600",{"doi":9,"openalex_id":9,"authors":162,"venue":9,"cited_by_count":35,"oa_url":9,"card":163,"direction":103,"ingested_from":168},[],{"tldr":164,"method":165,"finding":166,"direction":103,"opportunity":167},"研发基于冠层覆盖率的无人机自适应点喷系统，实现大豆田间精准除草。","用无人机冠层感知与阈值点喷控制，室内外估算覆盖度R²为0.91和0.69。","25%阈值下误喷激活率仅4.8%，室内自动喷雾执行准确率达97.3%。","可探索多作物、多生育期自适应阈值与杂草识别模型融合，提升田间鲁棒性。","agent","2026-09-27T00:05:18.158668Z",{"id":171,"title":172,"url":173,"summary":174,"summary_zh":9,"content":9,"source_name":175,"source_url":173,"published_at":11,"category":12,"cover_url":9,"hotness":113,"is_selected":14,"score":176,"score_detail":177,"sources":180,"tags":185,"search_phrases":187,"slug":190,"view_count":35,"doi":191,"paper":192,"created_at":209},3565,"An Automata-Driven Cognitive Explainable Artificial Intelligence Framework for Climate-Adaptive Precision Agriculture and Environmental Sustainability","https:\u002F\u002Fdoi.org\u002F10.7759\u002Fs44389-026-00295-5","An Automata-Driven Cognitive Explainable Artificial Intelligence Framework for Climate-Adaptive Precision Agriculture and Environmental Sustainability。Cureus Journal of Computer Science.","Cureus Journal of Computer Science.",39,{"impact":20,"substance":67,"depth":13,"authority":67,"freshness":178,"relevant":21,"comment":179},9,"主题契合智慧农业与农业AI，但摘要仅重复标题、无方法与数据细节，信息增量有限，暂不建议进入每日精选。",[181,182],{"name":175,"url":173},{"name":183,"url":184},"Cureus Journal of Computer Science 2026-09-25","https:\u002F\u002Fwww.cureusjournals.com\u002Farticles\u002F20543",[26,86,87,28,186],"气候适应",[188,189],"气候适应 精准农业 可解释AI","农业人工智能 智慧农业 气候适应 精准农业","气候适应精准农业可解释AI-3565","10.7759\u002Fs44389-026-00295-5",{"doi":191,"openalex_id":193,"authors":194,"venue":175,"cited_by_count":35,"oa_url":173,"card":9,"direction":63,"ingested_from":64},"W7214363342",[195,198,201,203,205,207],{"name":196,"orcid":197},"Mritunjay Kr. Ranjan","https:\u002F\u002Forcid.org\u002F0000-0003-0240-4909",{"name":199,"orcid":200},"Rohit Gupta","https:\u002F\u002Forcid.org\u002F0000-0002-4436-8275",{"name":202,"orcid":9},"Nitin  D Mali",{"name":204,"orcid":9},"Ansh  A Rajore",{"name":206,"orcid":9},"Gaurav Narendra Patil",{"name":208,"orcid":9},"Ankita  N Patil","2026-09-26T23:30:47.893666Z",{"id":211,"title":212,"url":213,"summary":214,"summary_zh":215,"content":9,"source_name":216,"source_url":213,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":217,"score":218,"score_detail":219,"sources":222,"tags":224,"search_phrases":227,"slug":230,"view_count":35,"doi":231,"paper":232,"created_at":254},3564,"Research Progress on Intelligent Seeding Technology and Equipment: The Development of Seeders from Multi-Functional Integration to Agricultural Intelligent Agents","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16191884","Seeding constitutes a key crop-production operation that governs seed spatial arrangement, crop population structure, and potential yield formation, and forms the foundation of precise, efficient, and eco-friendly farming. However, field soil properties, regional climate, and crop agronomic requirements exhibit strong spatio-temporal heterogeneity. Conventional seeding operations based on manual experience and fixed preset parameters cannot meet the demands of large-scale precision agriculture. Enabled by progress in precision agriculture, intelligent sensing, artificial intelligence, and autonomous machinery, modern intelligent seeding systems integrate precision seed metering, high-precision environmental perception, and closed-loop dynamic self-regulation. Such systems can improve plant-spacing uniformity and enable precise seeding-depth control under standard open-field conditions, yet face noticeable performance limitations in GNSS-denied complex environments including dense crop canopies and greenhouses. This review outlines the evolutionary trajectory of seeding machinery and summarizes research progress regarding precision seeding, multi-functional equipment integration, multi-source information perception, and intelligent decision-making. Integrated design principles covering mechanical optimization, electronic control, and perception-driven decision systems are elaborated. Four developmental phases of seeding equipment are identified: mechanical precision operation, electronic intelligent regulation, multi-functional module integration, and intelligent cognitive integration. Current intelligent seeding technologies are constrained by limited adaptability to complex farmland conditions, unstable multi-source data fusion, insufficient long-term operational reliability, and high deployment costs across diverse scenarios, restricting their broad field-scale adoption. Future research should combine agronomic knowledge with artificial intelligence to improve environmental awareness and autonomous decision-making capability, develop low-cost, high-reliability integrated seeding equipment, and support the construction of intelligent agricultural machinery systems.","播种是决定种子空间分布、作物群体结构和潜在产量形成的关键作物生产环节，也是精准、高效、绿色农业的基础。然而，田间土壤特性、区域气候和作物农艺要求具有强烈的时空异质性。基于人工经验和固定预设参数的传统播种作业无法满足大规模精准农业的需求。在精准农业、智能感知、人工智能和自主机械等领域的进步推动下，现代智能播种系统集成了精密排种、高精度环境感知和闭环动态自适应调节。此类系统可在标准露地条件下提高株距均匀性并实现精量播种深度控制，但在全球导航卫星系统（GNSS）拒止的复杂环境中，包括密植作物冠层和温室，仍面临明显的性能局限。本文综述了播种机械的演进轨迹，总结了精量播种、多功能装备集成、多源信息感知和智能决策方面的研究进展。阐述了涵盖机械优化、电子控制和感知驱动决策系统的集成设计原则。识别出播种装备的四个发展阶段：机械精量作业、电子智能调控、多功能模块集成和智能认知集成。当前智能播种技术受限于对复杂农田条件的适应性不足、多源数据融合不稳定、长期运行可靠性不够以及多场景部署成本高昂，制约了其在田间的广泛规模化应用。未来研究应将农艺知识与人工智能相结合，提升环境感知和自主决策能力，开发低成本、高可靠性的集成播种装备，支撑智能农机体系建设。","Agronomy",true,82,{"impact":18,"substance":220,"depth":17,"authority":81,"freshness":20,"relevant":21,"comment":221},21,"系统梳理智能播种装备从机械化到智能体四阶段演进，指出GNSS受限环境与多源数据融合瓶颈，对智慧农业装备研发有参考价值。",[223],{"name":216,"url":213},[26,86,225,28,226],"智能农机","智能播种",[228,229],"智能播种 装备","Agronomy 智能播种 装备","智能播种装备-3564","10.3390\u002Fagronomy16191884",{"doi":231,"openalex_id":233,"authors":234,"venue":216,"cited_by_count":35,"oa_url":213,"card":249,"direction":63,"ingested_from":64},"W7214297818",[235,237,239,241,243,246],{"name":236,"orcid":9},"Yuting Dong",{"name":238,"orcid":9},"Yapeng Wu",{"name":240,"orcid":9},"Shiguo Wang",{"name":242,"orcid":9},"Xiaohu Guo",{"name":244,"orcid":245},"Xin Lu","https:\u002F\u002Forcid.org\u002F0000-0003-4462-3472",{"name":247,"orcid":248},"Zhong Tang","https:\u002F\u002Forcid.org\u002F0000-0002-2724-115X",{"tldr":250,"method":251,"finding":252,"direction":103,"opportunity":253},"综述智能播种技术装备从多功能集成到农业智能体的四阶段演进及瓶颈。","文献综述，梳理精量播种、多源感知与智能决策的集成设计。","智能播种在开阔农田表现良好，但复杂环境下适应性、数据融合与成本仍受限。","GNSS拒止的冠层与温室环境下低成本高可靠感知与自主决策播种装备是研究空白。","2026-09-26T23:30:47.821171Z",{"id":256,"title":257,"url":258,"summary":259,"summary_zh":9,"content":9,"source_name":10,"source_url":258,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":260,"score_detail":261,"sources":263,"tags":265,"search_phrases":270,"slug":273,"view_count":35,"doi":274,"paper":275,"created_at":291},3523,"Soil-driven variability in crop response to variable-rate seeding and fertilization in an irrigated maize–sunflower system","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10458-y","Soil-driven variability in crop response to variable-rate seeding and fertilization in an irrigated maize–sunflower system。Precision Agriculture",69,{"impact":118,"substance":17,"depth":80,"authority":19,"freshness":20,"relevant":21,"comment":262},"精准农业核心期刊论文，探讨土壤空间变异对变量播种与施肥效果的影响，方法新颖、结论可靠，对智慧农业田间管理有参考价值，但属细分领域研究，影响范围有限。",[264],{"name":10,"url":258},[26,266,267,28,268,269],"变量施肥","玉米","向日葵","变量播种",[271,272],"Precision Agriculture 变量播种 变量施肥","灌溉玉米 向日葵 土壤变异","PrecisionAgriculture变量播种变量施肥-3523","10.1007\u002Fs11119-026-10458-y",{"doi":274,"openalex_id":276,"authors":277,"venue":10,"cited_by_count":35,"oa_url":9,"card":9,"direction":9,"ingested_from":64},"W7214357812",[278,281,283,285,287,289],{"name":279,"orcid":280},"María Videgain","https:\u002F\u002Forcid.org\u002F0000-0002-3630-7931",{"name":282,"orcid":9},"J. A. Martínez-Casasnovas",{"name":284,"orcid":9},"S. Artero",{"name":286,"orcid":9},"A. Vigo",{"name":288,"orcid":9},"M. Vidal",{"name":290,"orcid":9},"F. J. García-Ramos","2026-09-26T23:30:03.127069Z"]