[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3371":3,"related-3371":70},{"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":69},3371,"Optimizing nitrogen fertilization in mustard through GreenSeeker-based precision agriculture: Impacts on productivity, economics, and environmental sustainability","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0358762","Excessive nitrogen (N) fertilization is a major challenge to sustainable agriculture, adversely affecting crop productivity, soil quality, and environmental health, emits a potent greenhouse gas nitrous oxide (N 2 O), and nearly 80% of sectoral emissions linked to nutrient inputs applied during crop production. The escalating use of N fertilizers further amplifies N 2 O emissions, undermining soil sustainability and contributing to environmental degradation. So, the hypothesis used behind the study is increasing nitrogen use efficiency from its current level (approx. 30–40%) would reduce the need for N fertilizer requirement, resulting in more cost-effective and environmentally sustainable production system with no yield penalty. With this objective, a field study was executed in rabi 2021–2024, consisting of nine N management treatments and one unfertilized treatment as control, randomized in complete block design in three replicates. To overcome this problem, first we standardized NDVI based N application and developed a Nitrogen Estimation Rate Chart (NERC) for precision N management in real time for specific set target yield. Results showed that sensor based N application enhanced growth and yield attributes, registered 22.75% seed yield enhancement with 18.7% N saving over RDF and 34.4 & 30.2% increment in NMR and BCR. This treatment augmented AE N and PFP N by 14.18 to 124.7% and 1.78 to 51.1%, respectively achieved higher NRE (35–69.5%). The per cent enrichment in SOC content was found superior in RDN 100 + 2% Urea FS (52.6%) followed by RDN 100 + 1.5% KNO 3 FS (39.4%) statistically comparable to RDF (38.1%). The fertilizers contributed largest share of total carbon emissions, accounting for 45.6–52.3%. NDVI based N management reduced GHGs emissions by approximately 11.23% over RDF. The mean biological yield (seed + stover) unveiled positive correlation to total nitrogen (r 2 = 98), total phosphorus (r 2 = 95) and total potassium (r 2 = 98) uptake. This innovative technology enables farmers to prevent excessive N fertilization, thereby reducing resource wastage and minimizing nitrous oxide emission. This technology provides insight on N need of crop in real time in right quantity.","过量施氮是可持续农业面临的一项重大挑战，会对作物生产力、土壤质量和环境健康产生不利影响，并排放强效温室气体氧化亚氮（N₂O），而该领域近80%的排放与作物生产过程中投入的养分有关。氮肥用量的不断攀升进一步加剧了N₂O排放，削弱土壤可持续性并导致环境退化。因此，本研究提出的假设是：将氮利用效率从当前水平（约30%–40%）提高，可减少对氮肥的需求，从而在不造成产量损失的前提下，实现更具成本效益和环境可持续性的生产体系。基于此目标，于2021—2024年rabi季开展了一项田间研究，包括9个氮管理处理和1个不施肥对照处理，采用完全随机区组设计，3次重复。为解决上述问题，首先标准化了基于NDVI的氮肥施用方法，并开发了氮估算速率表（NERC），用于针对特定目标产量进行实时精准氮管理。结果表明，基于传感器的氮肥施用促进了生长和产量性状，种子产量较推荐施肥量（RDF）提高22.75%，节省氮肥18.7%，净收益（NMR）和效益成本比（BCR）分别提高34.4%和30.2%。该处理使氮农学效率（AE N）和氮偏生产力（PFP N）分别提高14.18%–124.7%和1.78%–51.1%，并实现了更高的氮回收效率（NRE，35%–69.5%）。土壤有机碳（SOC）含量的提升幅度以RDN 100 + 2%尿素叶面喷施（52.6%）最优，其次为RDN 100 + 1.5% KNO₃叶面喷施（39.4%），与RDF（38.1%）在统计上相当。肥料对总碳排放的贡献最大，占45.6%–52.3%。基于NDVI的氮管理较RDF减少温室气体排放约11.23%。平均生物产量（种子+秸秆）与总氮（r² = 98）、总磷（r² = 95）和总钾（r² = 98）吸收量呈正相关。这项创新技术使农民能够避免过量施氮，从而减少资源浪费并降低氧化亚氮排放。该技术可实时提供作物氮需求信息，并指导适宜用量的施用。",null,"PLoS ONE","2026-09-23T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,8,1,"基于NDVI传感器的实时精准施氮研究，四年田间试验数据扎实，兼具增产、节肥与减排价值，对智慧农业施肥决策有参考意义。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","精准施肥","氮肥管理","NDVI遥感","绿色低碳农业",[32,33],"GreenSeeker NDVI 氮肥管理","芥菜型油菜 精准施氮","GreenSeekerNDVI氮肥管理-3371",0,"10.1371\u002Fjournal.pone.0358762",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":6,"card":61,"direction":67,"ingested_from":68},"W7214115167",[40,43,45,47,49,51,53,55,57,59],{"name":41,"orcid":42},"V. D. Meena","https:\u002F\u002Forcid.org\u002F0000-0001-9528-4832",{"name":44,"orcid":9},"M.L. Dotaniya",{"name":46,"orcid":9},"M.D. Meena",{"name":48,"orcid":9},"R. S. Jat",{"name":50,"orcid":9},"MK Meena",{"name":52,"orcid":9},"R. L. Choudhary",{"name":54,"orcid":9},"H. V. Singh",{"name":56,"orcid":9},"HS MEENA",{"name":58,"orcid":9},"B.L. Meena",{"name":60,"orcid":9},"V. V. Singh",{"tldr":62,"method":63,"finding":64,"direction":65,"opportunity":66},"基于GreenSeeker的NDVI实时氮肥管理优化芥菜施氮，提升产量并减少氮肥与温室气体排放。","2021-2024年田间试验，9个氮处理+对照，用GreenSeeker NDV","传感器施氮使种子产量增22.75%、节氮18.7%，NRE达35-69.5%，温室气体减排约11.2","智慧农业 \u002F 农业物联网","可将NDVI实时氮管理扩展至其他作物与区域，并结合碳足迹模型量化减排经济价值。","农业绿色发展与碳","openalex","2026-09-24T23:30:41.022035Z",{"total":71,"page":21,"page_size":71,"items":72},6,[73,106,151,201,238,262],{"id":74,"title":75,"url":76,"summary":77,"summary_zh":9,"content":9,"source_name":78,"source_url":9,"published_at":79,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":80,"score_detail":81,"sources":86,"tags":88,"search_phrases":92,"slug":95,"view_count":35,"doi":9,"paper":96,"created_at":105},3326,"Research on the Application of Agricultural Big Data in Plant Growth Prediction——基于多源数据同化与混合智能（MDA-HI）框架","https:\u002F\u002Fwww.icck.org\u002Ffilebob\u002Fuploads\u002Fstorage\u002FDIA_ANrbHOHS4uBBpnVQ6.pdf","《Digital Intelligence in Agriculture》2026年第2卷第2期。Wei Yongqiang等提出Multi-source Data Assimilation and Hybrid Intelligence（MDA-HI）框架，将基于过程的作物模型与集成机器学习算法（包括基于Transformer的架构和物理信息神经网络）相结合。在2023—2025年中国多生态区主要作物（水稻、小麦、玉米、番茄）的实证验证中：MDA-HI模型实现了产量预测RMSE平均减少42.7%、关键物候期预测减少38.1%。水稻-小麦轮作系统大规模案例研究显示数据驱动处方可将氮肥使用减少22.5%、灌溉水减少18.3%，同时产量增加5.1%。","《Digital Intelligence in Agriculture》2026; 2(2):54-67","2026-09-17T00:00:00Z",85,{"impact":18,"substance":82,"depth":83,"authority":84,"freshness":71,"relevant":21,"comment":85},24,19,14,"多源数据同化与混合智能框架在四大作物上验证，减肥节水增产数据扎实，方法新颖且具产业推广价值。",[87],{"name":78,"url":76},[26,89,27,90,91],"农业人工智能","农业大数据","作物生长预测",[93,94],"MDA-HI 多源数据同化 作物模型","水稻小麦轮作 氮肥减量 产量预测","MDA-HI多源数据同化作物模型-3326",{"doi":9,"openalex_id":9,"authors":97,"venue":9,"cited_by_count":35,"oa_url":9,"card":98,"direction":102,"ingested_from":104},[],{"tldr":99,"method":100,"finding":101,"direction":102,"opportunity":103},"提出MDA-HI框架，融合过程模型与混合智能，用于作物生长与产量预测。","多源数据同化结合Transformer与物理信息神经网络，在中国多生态区验证。","产量预测RMSE降42.7%，氮肥减22.5%、灌溉水减18.3%，产量增5.1%。","农业人工智能与决策模型","可探索轻量化MDA-HI在边缘设备部署及跨区域迁移能力，降低小农户应用门槛。","agent","2026-09-24T00:04:02.947080Z",{"id":107,"title":108,"url":109,"summary":110,"summary_zh":111,"content":9,"source_name":112,"source_url":109,"published_at":79,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":113,"score_detail":114,"sources":119,"tags":121,"search_phrases":124,"slug":127,"view_count":35,"doi":128,"paper":129,"created_at":150},2805,"Synergistic effects of spectral preprocessing and machine learning algorithms for nitrogen estimation in tomato using hyperspectral spectral data","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-71908-1","Abstract Accurate estimation of leaf nitrogen content is essential for optimizing fertilization management and improving crop productivity. This study proposes a nondestructive hyperspectral imaging framework combined with advanced machine learning algorithms to classify nitrogen levels in tomato leaves (Solanum lycopersicum L., Royal variety). A total of 300 hyperspectral samples were collected from plants subjected to three nitrogen treatments (N-30%, N-60%, and N-90%) under controlled conditions. Reflectance data (400–1000 nm) were calibrated and preprocessed using Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), and Savitzky–Golay (SG) filtering. Both unsupervised and supervised algorithms were systematically evaluated. Clustering results demonstrated that preprocessing substantially influenced class separability. The MSC + GMM combination yielded the best unsupervised results, with the lowest Davies–Bouldin index (0.443) and the highest silhouette coefficient (0.975), indicating improved cluster compactness and separation. In supervised learning, Neural Networks (NN) consistently outperformed the other models, achieving 100% accuracy and an ROC–AUC of 1.00 under SNV, MSC, and SG preprocessing. Gradient Boosting (GB) and Support Vector Machine (SVM) also demonstrated strong predictive capability, whereas conventional models, including LDA, LR, KNN, and NB, showed moderate improvements following preprocessing. Statistical analysis confirmed the significant effect of spectral preprocessing on both clustering and classification outcomes ( p \u003C 0.05). Overall, integrating hyperspectral imaging with appropriate preprocessing and nonlinear machine learning models provided strong classification performance under the controlled experimental conditions. However, the exceptionally high performance observed for some models should be interpreted cautiously because of the relatively small dataset and experimentally controlled nitrogen treatments. Moreover, the absence of an independent external validation dataset limits the assessment of generalizability across growing conditions, cultivars, and field environments. Therefore, independent validation using larger and more diverse datasets is required before broader application of the proposed framework in practical precision agriculture.","摘要 准确估算叶片氮含量对于优化施肥管理和提高作物生产力至关重要。本研究提出了一种无损高光谱成像框架，结合先进的机器学习算法对番茄叶片（Solanum lycopersicum L.，Royal品种）的氮水平进行分类。在受控条件下，从接受三种氮处理（N-30%、N-60%和N-90%）的植株中共采集了300个高光谱样本。反射率数据（400–1000 nm）经标准正态变量变换（SNV）、多元散射校正（MSC）和Savitzky–Golay（SG）滤波进行校准和预处理。系统评估了无监督和有监督算法。聚类结果表明，预处理对类别可分性产生了显著影响。MSC + GMM组合取得了最佳无监督结果，具有最低的Davies–Bouldin指数（0.443）和最高的轮廓系数（0.975），表明聚类紧密度和分离度得到改善。在有监督学习中，神经网络（NN）始终优于其他模型，在SNV、MSC和SG预处理下均达到100%的准确率和1.00的ROC–AUC。梯度提升（GB）和支持向量机（SVM）也表现出较强的预测能力，而传统模型（包括LDA、LR、KNN和NB）在预处理后表现出中等程度的改善。统计分析证实了光谱预处理对聚类和分类结果均有显著影响（p \u003C 0.05）。总体而言，将高光谱成像与适当的预处理及非线性机器学习模型相结合，在受控实验条件下提供了较强的分类性能。然而，由于数据集相对较小且氮处理为实验受控条件，某些模型所表现出的极高性能应谨慎解读。此外，缺乏独立的外部验证数据集限制了对跨生长条件、品种和田间环境泛化能力的评估。因此，在将该框架广泛应用于实际精准农业之前，需要使用更大规模、更多样化的数据集进行独立验证。","Scientific Reports",77,{"impact":115,"substance":116,"depth":117,"authority":84,"freshness":13,"relevant":21,"comment":118},15,21,17,"高光谱结合机器学习实现番茄叶片氮素无损估测，方法系统、结论明确，但样本量小且缺乏外部验证，属细分领域方法学进展。",[120],{"name":112,"url":109},[26,89,27,122,123],"番茄种植","高光谱遥感",[125,126],"农业人工智能 高光谱遥感 智慧农业 番茄种植","农业人工智能 高光谱遥感","农业人工智能高光谱遥感智慧农业番茄种植-2805","10.1038\u002Fs41598-026-71908-1",{"doi":128,"openalex_id":130,"authors":131,"venue":112,"cited_by_count":35,"oa_url":109,"card":144,"direction":102,"ingested_from":68},"W7213472002",[132,135,138,141],{"name":133,"orcid":134},"Mohammad Vahedi Torshizi","https:\u002F\u002Forcid.org\u002F0000-0003-3648-1515",{"name":136,"orcid":137},"Sajad Sabzi","https:\u002F\u002Forcid.org\u002F0000-0003-2439-5329",{"name":139,"orcid":140},"Mohsen Azadbakht","https:\u002F\u002Forcid.org\u002F0000-0002-5726-9321",{"name":142,"orcid":143},"Razieh Pourdarbani","https:\u002F\u002Forcid.org\u002F0000-0003-0766-8305",{"tldr":145,"method":146,"finding":147,"direction":148,"opportunity":149},"用高光谱成像结合预处理与机器学习，对番茄叶片氮素水平进行分类。","300个高光谱样本，SNV、MSC、SG预处理，聚类与多种监督分类算法对比。","MSC+GMM聚类最优，神经网络在三种预处理下均达100%准确率。","农业遥感与作物表型","样本少且无外部验证，可扩展多品种、多环境田间数据并做独立验证以提升泛化性。","2026-09-17T23:30:59.361085Z",{"id":152,"title":153,"url":154,"summary":155,"summary_zh":9,"content":9,"source_name":156,"source_url":154,"published_at":157,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":158,"sources":161,"tags":163,"search_phrases":166,"slug":169,"view_count":21,"doi":170,"paper":171,"created_at":200},2622,"Synergizing process-based modeling and data-driven learning for precision nitrogen optimization in winter wheat","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104983","Synergizing process-based modeling and data-driven learning for precision nitrogen optimization in winter wheat。Agricultural Systems","Agricultural Systems","2026-09-16T00:00:00Z",{"impact":17,"substance":159,"depth":117,"authority":84,"freshness":13,"relevant":21,"comment":160},20,"将过程模型与数据驱动学习融合用于冬小麦精准氮肥优化，方法新颖、发表于核心期刊且时效性强，具备进入每日精选的价值。",[162],{"name":156,"url":154},[26,89,27,164,165],"作物模型","冬小麦",[167,168],"农业人工智能 作物模型 智慧农业 精准施肥","农业人工智能 作物模型","农业人工智能作物模型智慧农业精准施肥-2622","10.1016\u002Fj.agsy.2026.104983",{"doi":170,"openalex_id":172,"authors":173,"venue":156,"cited_by_count":35,"oa_url":154,"card":9,"direction":9,"ingested_from":68},"W7213352238",[174,176,179,182,184,186,188,190,192,194,196,198],{"name":175,"orcid":9},"Yuru Ye",{"name":177,"orcid":178},"Qian Wang","https:\u002F\u002Forcid.org\u002F0000-0003-0750-7843",{"name":180,"orcid":181},"Davide Cammarano","https:\u002F\u002Forcid.org\u002F0000-0003-0918-550X",{"name":183,"orcid":9},"Kang Yu",{"name":185,"orcid":9},"Siva K. Balasundram",{"name":187,"orcid":9},"Wei Li",{"name":189,"orcid":9},"Xiuli Li",{"name":191,"orcid":9},"Xiaojun Liu",{"name":193,"orcid":9},"Yongchao Tian",{"name":195,"orcid":9},"Yan Zhu",{"name":197,"orcid":9},"Weixing Cao",{"name":199,"orcid":9},"Qiang Cao","2026-09-16T23:30:05.340754Z",{"id":202,"title":203,"url":204,"summary":205,"summary_zh":206,"content":9,"source_name":207,"source_url":204,"published_at":208,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":209,"score_detail":210,"sources":214,"tags":216,"search_phrases":219,"slug":222,"view_count":35,"doi":223,"paper":224,"created_at":237},2517,"Artificial Intelligence-Based Intelligent Fertigation Recommendation Systems for Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.64388\u002Firev10i3-1722972","Artificial-intelligence-enabled fertilizer and fertigation recommendation has progressed from single-task crop or fertilizer classification toward integrated decision-support architectures. However, predictive classification, agronomic dose calculation, irrigation scheduling, explainability, real-time sensing, and farmer-facing delivery are often studied separately. This paper presents a structured narrative review of these strands and an applied machine-learning case study using the publicly available Crop and Fertilizer Dataset for Western Maharashtra. The case study contains 4,513 records spanning five districts, 16 crops, and 19 fertilizer classes. Seven classifiers were compared using an 80:20 stratified train-test split with five-fold stratified cross-validation on the training set. XGBoost achieved 97.34% test accuracy and 94.21% ± 1.15% cross-validated accuracy, while Random Forest achieved 93.58% and 90.89% ± 0.80%, respectively. TreeSHAP analysis of Random Forest identified crop identity, potassium, and nitrogen as the leading predictors of the historical fertilizer class. These results are interpreted as a computational baseline rather than proof of agronomic optimality because the target label represents recorded fertilizer choices. The review also incorporates evidence on IoT\u002Fedge-cloud sensing, multilingual agricultural advisory, and federated learning. It concludes that RF\u002FSHAP, IoT sensing, and multilingual interfaces are established capabilities; a more defensible research direction is an auditable pipeline that separates fertilizer identity, nutrient dose, and application timing, connects explainable prediction to sequential scheduling, and independently benchmarks outputs against authoritative agronomic guidance. The proposed Intelligent Fertigation Recommendation System (IFRS) is therefore presented as a research framework requiring multi-season and field validation before claims of yield, water, nutrient-use-efficiency, or adoption benefits.","人工智能驱动的肥料与灌溉施肥推荐已从单一任务的作物或肥料分类，发展为集成式决策支持架构。然而，预测性分类、农艺用量计算、灌溉调度、可解释性、实时感知以及面向农户的交付往往被分别研究。本文对这些研究脉络进行了结构化叙述性综述，并基于公开的“西马哈拉施特拉邦作物与肥料数据集”开展了一项应用机器学习案例研究。该案例研究包含4，513条记录，涵盖五个地区、16种作物和19个肥料类别。采用80：20分层训练-测试划分，并在训练集上进行五折分层交叉验证，比较了七种分类器。XGBoost取得了97.34%的测试准确率和94.21% ± 1.15%的交叉验证准确率，而随机森林分别取得了93.58%和90.89% ± 0.80%。对随机森林的TreeSHAP分析表明，作物身份、钾和氮是历史肥料类别的主要预测因子。这些结果被解释为计算基线，而非农艺最优性的证明，因为目标标签代表的是有记录的肥料选择。该综述还纳入了关于物联网\u002F边缘-云感知、多语言农业咨询和联邦学习的证据。结论认为，随机森林\u002FSHAP、物联网感知和多语言界面已是成熟能力；更具可辩护性的研究方向是构建一条可审计的流水线，将肥料身份、养分用量和施用时机分离，将可解释预测与序贯调度相连接，并依据权威农艺指南对输出进行独立基准测试。因此，所提出的智能灌溉施肥推荐系统（IFRS）被作为一个研究框架提出，在声称产量、水分、养分利用效率或采用效益之前，仍需进行多季和田间验证。","Iconic Research and Engineering Journals","2026-09-14T00:00:00Z",62,{"impact":211,"substance":17,"depth":117,"authority":71,"freshness":212,"relevant":21,"comment":213},12,9,"对AI水肥推荐研究进行系统综述并给出可复现的机器学习基线，方法透明、结论审慎，但来源为普通工程类期刊且属综述性论文，产业影响有限，适合作为技术参考而非每日精选头条。",[215],{"name":207,"url":204},[26,89,27,217,218],"可解释AI","水肥一体化",[220,221],"农业人工智能 水肥一体化 智慧农业 精准施肥","农业人工智能 水肥一体化","农业人工智能水肥一体化智慧农业精准施肥-2517","10.64388\u002Firev10i3-1722972",{"doi":223,"openalex_id":225,"authors":226,"venue":207,"cited_by_count":35,"oa_url":231,"card":232,"direction":65,"ingested_from":68},"W7212934364",[227,229],{"name":228,"orcid":9},"Shraddha S. Tayade",{"name":230,"orcid":9},"Yogesh V. Chimate","https:\u002F\u002Fwww.irejournals.com\u002Fformatedpaper\u002F1722972.pdf",{"tldr":233,"method":234,"finding":235,"direction":102,"opportunity":236},"综述AI施肥推荐研究，并用马哈拉施特拉数据集比较七种分类器，提出可审计智能施肥推荐框架。","结构化综述加机器学习案例，4513条记录，七分类器对比，XGBoost与Tree","XGBoost测试准确率97.34%，但标签仅为历史施肥选择，不能证明农艺最优。","可研究分离肥料种类、养分剂量与施用时序的可审计推荐流水线，并进行多季田间验证。","2026-09-15T23:30:12.675690Z",{"id":239,"title":240,"url":241,"summary":242,"summary_zh":9,"content":243,"source_name":244,"source_url":9,"published_at":245,"category":246,"cover_url":9,"hotness":13,"is_selected":14,"score":247,"score_detail":248,"sources":251,"tags":253,"search_phrases":257,"slug":260,"view_count":35,"doi":9,"paper":9,"created_at":261},2355,"黑龙江省农科院佳木斯分院金秋博览会启幕:5000名种地'老把式'田间取经","https:\u002F\u002Fwww.haas.cn\u002Finfo\u002F6401\u002F336021.htm","9月10日黑龙江省农科院佳木斯分院举办金秋博览会田间观摩会，70位专家现场答疑，5000余种地大户观摩智能除草机器人、气吸式智能播种机等装备；省农科院土壤肥料研究所所长王囡命基于TRPF系统的大豆精准施肥技术为农民及农业科研提供施肥处方8000余份，平均增产10%。","9月10日上午，省农科院佳木斯分院的粮油作物单产提升教练田里人山人海，一场别开生面的金秋田间观摩博览会在这里启幕。现场，智能除草机器人，气吸式智能播种机等智能装备，让5000余名种地“老把式”们看直了眼。70位农业专家被围得里三层外三层，玉米、大豆新品种产量咋样，肥料咋使，病虫害咋防，农机咋选，他们一样样讲，一遍遍答，让农民朋友听入了迷。\n\n一场开在田间地头的“农科盛宴”，打通了农业科技成果转化的“最后一公里”，为农民增产增收筑牢了根基，也为粮食安全“压舱石”增添了底气。\n\n**良种配良肥，丰收“底气”田间来**\n\n看到实验田里的“梅亚合玉44”丰收了，王春风走上前，上下打量株高，又小心翼翼地从玉米棒抠下几粒种子，放在手心里翻来覆去地看。“黄粮、棒大、轴细、马牙粒，一看就是好种子。”这位来自佳木斯市郊区四丰镇的农户说，去年村里有人种了这个品种，产量高、抗倒伏能力强，“我打算明年种上上30亩。”\n\n![Image 1](https:\u002F\u002Fwww.haas.cn\u002F__local\u002F7\u002FD9\u002F42\u002FF21A21376B42C395104183EFDEF_3391D2DD_1C2AD.jpeg?e=.jpeg)\n“合玉44”是省农科院佳木斯分院选育的玉米种子之一，种子经营权由梅亚种业独家买断。“去年，有农户来买玉米种子，点名要合玉44。当时，这款省农科院佳木斯分院培育的玉米种子刚审定，知名度还不高。我挺纳闷，一打听才知道，他是在‘金秋博览会’上看见的。”今年公司销售经理王慧带着团队首次参加博览会。她说，富锦来观摩的农民，来了好几台大客车，大伙儿都认这个活动。“省农科院佳木斯分院不仅培育出好品种，还帮经销商和农户搭建了交流、合作的平台。”\n\n一家来自辽宁沈阳的菌肥摊位上，围满了农户和经销商。这是企业负责人马莹第五次参加“金秋博览会”。“每次参展，都收获满满。这次我们带来了十几款微生物肥，得到很多农民朋友的青睐。”她说，一上午的时间，已经有十多家经销商，留下联系方式，表达了想要进一步合作的意愿。\n\n省农科院佳木斯分院土壤肥料研究所所长王囡囡蹲在试验田边，扒开一垄大豆根际的黑土：“我们依托‘基于TRPF系统的大豆精准施肥技术’，为农民及农业科研提供施肥处方8000余份，平均增产10%，被省农业农村厅推介为黑龙江省主推技术。”眼下，她所在的团队正结合无人机、遥感等技术，研发作物营养诊断及智能施肥模型，研究变量施肥处方图——“将来的‘处方’，能精确到每一块地、每一垄。”\n\n**智能农机显身手黑土地种出“科技范儿”**\n\n由兵器工业集团哈一机集团北方防务公司与杭州灿禾农科技有限公司联合研制的智能除草机器人，也亮相了本次博览会。\n\n灿禾农科技工作人员于晓涵介绍，支撑除草机器人顶部的高清摄像头结合AI视觉识别模块，一眼就能分清草和苗；图像信息传给智能控制器，机器再结合深度传感器与算法，在复杂地形里自主规划行进路线。“机器的行距可设定到15厘米到90厘米之间，适合大豆、玉米、马铃薯、甜菜等多种作物种植环境。精准除草，能让农作物增产10%—15%。这台机器已经卖到了黑龙江、内蒙古、新疆等农业主产区。”\n\n![Image 2](https:\u002F\u002Fwww.haas.cn\u002F__local\u002FD\u002F58\u002FE1\u002FA5219F99AFBA5ECE78A7CC78178_C419D505_33F89.jpeg?e=.jpeg)\n黑龙江省众为农机有限公司带来的一款世耕电驱气吸式智能播种机也颇具人气。总经理李杰介绍，这款播种机可实现“一机两用”，能播2行大垄的玉米，也可以播3行大垄的大豆。“去年，农科院教练田，就是用我们机器播种。这次展会，很多农户对我们的机器非常感兴趣，他们问了机器的价格、参数，希望到厂里参观。”李杰说。\n\n“这是我们联合科研单位、涉农企业，给农民打造一站式的服务盛会。”省农科院佳木斯分院院长丁俊杰介绍，在这次活动中，从智能播种机，到大豆、水稻和玉米品种的选育，再到高产栽培技术和病虫害防治，田间地头都能看到。\n\n他认为，让几千个种地大户同时来观摩最新的技术产品实验，农民“短平快”地在这里学到耕、种、管、收的科学知识，意义很大。“农民朋友刚开始来，是照着葫芦画瓢；随着参加届数的增加，他们从照着葫芦画瓢，变成了照着葫芦画葫芦。”\n\n“不怕农民朋友学得慢，就怕我们工作不持续。”丁俊杰感慨道，“金秋博览会”一届一届办下来，变化写在农民的车轮上。“有农户从开捷达来，到开路虎来，这就是举办活动的意义所在。让农民增产增收，农民富了，国家才能更强，粮食安全才能得到保障。”\n\n正午，田埂上的讲解还在继续，专家讲得细，农民问得勤。这场开在田间地头的博览会，正把科技的种子撒进泥土，长成沉甸甸的丰收。","黑龙江省农业科学院 | 2026-09-10","2026-09-10T00:00:00Z","报道",66,{"impact":249,"substance":117,"depth":115,"authority":211,"freshness":71,"relevant":21,"comment":250},16,"省级农科院田间博览会报道，智能除草机器人、电驱气吸播种机与精准施肥技术等细节充实，多方信源扎实，但属区域活动、时效稍旧，适合主题聚合而非头条精选。",[252],{"name":244,"url":241},[26,27,254,255,256],"农业科技成果转化","智能农机","种业振兴",[258,259],"农业科技成果转化 智慧农业 智能农机 种业振兴","农业科技成果转化 智慧农业","农业科技成果转化智慧农业智能农机种业振兴-2355","2026-09-14T00:06:26.437168Z",{"id":263,"title":264,"url":265,"summary":266,"summary_zh":267,"content":9,"source_name":268,"source_url":265,"published_at":245,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":269,"score_detail":270,"sources":272,"tags":274,"search_phrases":278,"slug":281,"view_count":35,"doi":282,"paper":283,"created_at":293},2332,"Wheat Nitrogen Fertilizer Management Using GreenSeeker Handheld Crop Canopy Sensor","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijaes.vol.11.no3.2025.pg12.22","One of the essential factors in increasing agricultural yields of cereal crops is increasing the grain yield without increasing production costs. Wheat (Triticum aestivum L.) is the main crop in the food basket of the entire world. Therefore, it is necessary to determine the appropriate nitrogen (N) requirements to obtain the optimal production of wheat crops by investigating the impact of different N levels on the wheat crop yield in the Al-Muthanna region, as well as determining the possibility of predicting grain yield using the GreenSeeker handheld proximal crop canopy sensorbased differences vegetative difference index (NDVI). Thus, there is a need to re-evaluate the previous recommendations using remote sensing techniques. The experimental treatments were five levels of N fertilizer including (0 Kg N ha-1 , 50 kg N ha-1 , 100 kg N ha-1 ,150 kg N ha-1, and 200 kg N ha-1, and each N level was divided into 100%, 70%, 50% percentages, respectively. This study was conducted at the Experiment Station and Agriculture Research of the College of AgricultureAl-Muthanna University. The NDVI measurements were obtained at FK5, FK7, and FK9 according to the Feekes scale growth stage. The results indicated significant differences in grain yield between different levels of N fertilizer and a 70% percentage outperformed on the 100% and 50% treatments for each of the two N levels of 200 and 150 kg ha-1 . The results also show significant differences between NDVI values for different N fertilizer levels. The NDVI readings and wheat yield values increased and followed a similar pattern with increasing N fertilizer levels. This suggests that NDVI can predict wheat grain yield when the NDVI is not saturated. This study showed the potential of using GreenSeeker proximal crop canopy sensor-based NDVI readings as a useful tool to predict wheat grain yield.","提高谷类作物农业产量的关键因素之一是在不增加生产成本的前提下提高谷物产量。小麦（Triticum aestivum L.）是全球粮食结构中的主要作物。因此，有必要通过研究不同氮水平对Al-Muthanna地区小麦作物产量的影响，确定获得小麦作物最佳产量所需的适宜氮（N）需求量，并确定利用基于GreenSeeker手持式近地作物冠层传感器的归一化植被差异指数（NDVI）预测谷物产量的可能性。因此，需要利用遥感技术重新评估以往的推荐方案。试验处理包括五个氮肥水平（0 kg N ha⁻¹、50 kg N ha⁻¹、100 kg N ha⁻¹、150 kg N ha⁻¹和200 kg N ha⁻¹），每个氮水平分别按100%、70%、50%的比例施用。本研究在Al-Muthanna大学农学院实验站与农业研究中心进行。NDVI测定根据Feekes尺度生育阶段在FK5、FK7和FK9时期进行。结果表明，不同氮肥水平间谷物产量存在显著差异，在200和150 kg ha⁻¹两个氮水平下，70%施用量处理均优于100%和50%处理。结果还显示，不同氮肥水平间NDVI值存在显著差异。随着氮肥水平的提高，NDVI读数和小麦产量值均增加并呈现相似的变化趋势。这表明在NDVI未饱和时，NDVI可以预测小麦谷物产量。本研究表明，利用基于GreenSeeker近地作物冠层传感器的NDVI读数作为预测小麦谷物产量的有用工具具有潜力。","INTERNATIONAL JOURNAL OF AGRICULTURE AND EARTH SCIENCE",61,{"impact":211,"substance":17,"depth":115,"authority":13,"freshness":71,"relevant":21,"comment":271},"基于GreenSeeker手持冠层传感器NDVI预测小麦产量并优化氮肥用量，方法实用但属区域性田间试验，产业影响有限。",[273],{"name":268,"url":265},[26,27,275,276,277],"小麦","遥感","NDVI",[279,280],"智慧农业 精准施肥 小麦 遥感","智慧农业 精准施肥","智慧农业精准施肥小麦遥感-2332","10.56201\u002Fijaes.vol.11.no3.2025.pg12.22",{"doi":282,"openalex_id":284,"authors":285,"venue":268,"cited_by_count":35,"oa_url":9,"card":288,"direction":148,"ingested_from":68},"W7212289620",[286],{"name":287,"orcid":9},"Mohammed A. Naser",{"tldr":289,"method":290,"finding":291,"direction":148,"opportunity":292},"用GreenSeeker手持冠层传感器NDVI预测小麦产量并优化氮肥管理。","伊拉克田间试验，5个氮水平，Feekes 5\u002F7\u002F9期测NDVI。","NDVI与产量随氮肥增加同步上升，未饱和时可预测产量，70%氮量表现更优。","可探索NDVI饱和条件下的替代植被指数及不同品种\u002F气候区的氮肥推荐模型迁移。","2026-09-13T23:30:26.260534Z"]