[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3152":3,"related-3152":47},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"search_phrases":30,"slug":33,"view_count":34,"doi":35,"paper":36,"created_at":46},3152,"An integrated IoT and machine learning framework for maize yield prediction and smart agriculture in Tanzania","https:\u002F\u002Fdoi.org\u002F10.1080\u002F23311932.2026.2725400","An integrated IoT and machine learning framework for maize yield prediction and smart agriculture in Tanzania。Cogent Food & Agriculture",null,"Cogent Food & Agriculture","2026-09-22T00:00:00Z","论文",10,false,69,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":12,"relevant":20,"comment":21},12,18,16,13,1,"论文提出物联网与机器学习融合的玉米产量预测框架，方法有新意但属区域性应用研究，影响力有限，时效性强。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","产量预测","物联网","玉米",[31,32],"坦桑尼亚 玉米 产量预测","物联网 机器学习 智慧农业","坦桑尼亚玉米产量预测-3152",0,"10.1080\u002F23311932.2026.2725400",{"doi":35,"openalex_id":37,"authors":38,"venue":9,"cited_by_count":34,"oa_url":6,"card":8,"direction":44,"ingested_from":45},"W7213978365",[39,42],{"name":40,"orcid":41},"Alcardo Alex Barakabitze","https:\u002F\u002Forcid.org\u002F0000-0001-8960-8415",{"name":43,"orcid":8},"Yasinta Nzogera","智慧农业 \u002F 农业物联网","openalex","2026-09-22T23:30:10.178097Z",{"total":48,"page":20,"page_size":48,"items":49},6,[50,110,164,223,256,294],{"id":51,"title":52,"url":53,"summary":54,"summary_zh":55,"content":8,"source_name":56,"source_url":53,"published_at":57,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":58,"score_detail":59,"sources":65,"tags":67,"search_phrases":69,"slug":72,"view_count":34,"doi":73,"paper":74,"created_at":109},2505,"A continually evolving knowledge-guided deep learning framework for daily maize yield formation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104981","CONTEXT As multi-source agricultural datasets expand, accurate and interpretable simulation of crop yield formation is increasingly feasible and important for food security. Process-based models are widely used for yield forecasting but require laborious regional calibration. Artificial intelligence models learn physiological patterns from large datasets but lack physiological consistency and generalize poorly under data-scarce conditions. OBJECTIVE We developed AgroEvoDeep-Yield, a knowledge-guided model that encodes mechanistic understanding through its neural architecture, enabling explicit and interpretable simulation of daily maize yield formation from flowering to maturity. We further established a framework that supports continual model evolution as new data become available. METHODS The model was pretrained on APSIM-simulated grain number and daily grain biomass sequences to impose physiological consistency. Following pretraining, the model evolved through two continual learning stages. First, field-level continual learning using 796 field observations reduced biases inherited from APSIM. The model was then adapted to regional-scale multi-source inputs using long-term records from 945 meteorological stations and expert-constrained cultivar-parameter optimization. RESULTS AND CONCLUSIONS After field-level continual learning, AgroEvoDeep-Yield reduced grain number and yield simulation errors by 29% and 24%, respectively, relative to APSIM, and outperformed conventional machine learning models in out-of-region prediction. Region-level continual learning reduced yield RMSE to 1.09 t\u002Fha, and expert-constrained cultivar-parameter optimization further reduced it to 0.80 t\u002Fha. SIGNIFICANCE This study demonstrates how a knowledge-guided model can be repeatedly adapted as new data become available, providing a practical framework for continual model evolution and a basis for future agricultural decision support.","背景 随着多源农业数据集的不断扩展，准确且可解释地模拟作物产量形成过程日益可行，对粮食安全也愈发重要。基于过程的模型广泛用于产量预测，但需要耗费大量精力进行区域校准。人工智能模型能够从大规模数据集中学习生理模式，但缺乏生理一致性，在数据稀缺条件下泛化能力较差。目标 我们开发了AgroEvoDeep-Yield，这是一种知识引导模型，通过其神经网络架构编码机理认识，能够对玉米从开花到成熟的逐日产量形成过程进行显式且可解释的模拟。我们还建立了一个框架，以支持在新数据可用时模型的持续演化。方法 该模型在APSIM模拟的粒数和逐日籽粒生物量序列上进行预训练，以施加生理一致性约束。预训练后，模型通过两个持续学习阶段进行演化。首先，利用796个田间观测数据进行田块级持续学习，以减少继承自APSIM的偏差。随后，利用945个气象站的长期记录和专家约束的品种参数优化，将模型适配到区域尺度多源输入。结果与结论 经过田块级持续学习后，AgroEvoDeep-Yield相对于APSIM将粒数和产量模拟误差分别降低了29%和24%，并在区域外预测中优于传统机器学习模型。区域级持续学习将产量RMSE降至1.09 t\u002Fha，专家约束的品种参数优化进一步将其降至0.80 t\u002Fha。意义 本研究表明，知识引导模型可以在新数据可用时被反复适配，为模型持续演化提供了实用框架，并为未来农业决策支持奠定了基础。","Agricultural Systems","2026-09-14T00:00:00Z",85,{"impact":60,"substance":61,"depth":17,"authority":62,"freshness":63,"relevant":20,"comment":64},22,23,14,8,"知识引导的深度学习框架实现玉米逐日产量形成模拟，误差显著低于APSIM且支持持续学习，方法新颖、数据规模扎实，对智慧农业决策支持有参考价值。",[66],{"name":56,"url":53},[25,26,27,29,68],"作物模型",[70,71],"农业人工智能 产量预测 作物模型 智慧农业","农业人工智能 产量预测","农业人工智能产量预测作物模型智慧农业-2505","10.1016\u002Fj.agsy.2026.104981",{"doi":73,"openalex_id":75,"authors":76,"venue":56,"cited_by_count":34,"oa_url":53,"card":103,"direction":107,"ingested_from":45},"W7212532256",[77,79,81,84,87,90,92,95,97,100],{"name":78,"orcid":8},"Junji Ou",{"name":80,"orcid":8},"Wenyao Yan",{"name":82,"orcid":83},"Fangzheng Chen","https:\u002F\u002Forcid.org\u002F0009-0006-5061-6227",{"name":85,"orcid":86},"Tao Ye","https:\u002F\u002Forcid.org\u002F0000-0002-5037-8410",{"name":88,"orcid":89},"Ke Liu","https:\u002F\u002Forcid.org\u002F0000-0002-8343-0449",{"name":91,"orcid":8},"Matthew Tom Harrison",{"name":93,"orcid":94},"William D. Batchelor","https:\u002F\u002Forcid.org\u002F0000-0002-3881-6246",{"name":96,"orcid":8},"Yong Chen",{"name":98,"orcid":99},"Kelin Hu","https:\u002F\u002Forcid.org\u002F0000-0001-9321-0821",{"name":101,"orcid":102},"Puyu Feng","https:\u002F\u002Forcid.org\u002F0000-0003-4845-9876",{"tldr":104,"method":105,"finding":106,"direction":107,"opportunity":108},"提出知识引导的深度学习框架AgroEvoDeep-Yield，实现玉米逐日产量形成的可解释模拟与持续","基于APSIM模拟数据预训练，结合796个田间观测和945个气象站数据持续学习。","田间持续学习使粒数和产量误差比APSIM降低29%和24%，区域优化后RMSE降至0.80 t\u002Fha","农业人工智能与决策模型","可探索将知识引导与持续学习框架迁移至其他作物，并融合实时遥感与物联网数据实现动态产量预测。","2026-09-15T23:30:04.611750Z",{"id":111,"title":112,"url":113,"summary":114,"summary_zh":115,"content":8,"source_name":116,"source_url":113,"published_at":117,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":118,"score_detail":119,"sources":121,"tags":123,"search_phrases":125,"slug":127,"view_count":34,"doi":128,"paper":129,"created_at":163},2016,"Transfer learning integrated with SCOPE model for maize yield prediction based on spectral data for different spatial scales","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112381","Transfer learning integrated with SCOPE model for maize yield prediction based on spectral data for different spatial scales。Computers and Electronics in Agriculture","基于光谱数据的不同空间尺度玉米产量预测中，将迁移学习与SCOPE模型相结合","Computers and Electronics in Agriculture","2026-09-09T00:00:00Z",80,{"impact":17,"substance":60,"depth":17,"authority":62,"freshness":63,"relevant":20,"comment":120},"将迁移学习与SCOPE辐射传输模型结合，实现跨空间尺度光谱数据的玉米产量预测，方法新颖且对遥感估产有实用价值，值得进入每日精选。",[122],{"name":116,"url":113},[25,26,27,29,124],"遥感",[126,71],"农业人工智能 产量预测 智慧农业 玉米","农业人工智能产量预测智慧农业玉米-2016","10.1016\u002Fj.compag.2026.112381",{"doi":128,"openalex_id":130,"authors":131,"venue":116,"cited_by_count":34,"oa_url":8,"card":157,"direction":161,"ingested_from":45},"W7211999297",[132,134,137,140,142,144,146,149,151,153,155],{"name":133,"orcid":8},"Ruomei Zhao",{"name":135,"orcid":136},"Yanling Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-2323-2600",{"name":138,"orcid":139},"Weimin Guo","https:\u002F\u002Forcid.org\u002F0000-0003-4318-7154",{"name":141,"orcid":8},"Wei Liu",{"name":143,"orcid":8},"Long Zhao",{"name":145,"orcid":8},"Xiaoyuan Tian",{"name":147,"orcid":148},"Yelu Zeng","https:\u002F\u002Forcid.org\u002F0000-0003-4267-1841",{"name":150,"orcid":8},"Lulu An",{"name":152,"orcid":8},"Aiguo Wang",{"name":154,"orcid":8},"Qiang Xu",{"name":156,"orcid":8},"Hong Sun",{"tldr":158,"method":159,"finding":160,"direction":161,"opportunity":162},"结合迁移学习与SCOPE模型，利用不同空间尺度光谱数据预测玉米产量。","迁移学习与SCOPE辐射传输模型，多尺度光谱数据。","该方法能跨空间尺度提升玉米产量预测精度。","农业遥感与作物表型","可探索迁移学习在跨区域、跨年份及多作物产量预测中的泛化能力。","2026-09-10T23:30:01.419872Z",{"id":165,"title":166,"url":167,"summary":168,"summary_zh":169,"content":8,"source_name":170,"source_url":167,"published_at":171,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":172,"score_detail":173,"sources":175,"tags":177,"search_phrases":180,"slug":183,"view_count":34,"doi":184,"paper":185,"created_at":222},3199,"Recommendation of Suitable Planting Locations for Hybrid Maize Varieties Based on Graph Collaborative Filtering","https:\u002F\u002Fdoi.org\u002F10.1093\u002Finsilicoplants\u002Fdiag026","Abstract Enhancing the precision of crop variety adaptability evaluation is critical for maximizing yields and ensuring food security under climate change. Traditional methods relying on expert knowledge or process-based models face scalability challenges and difficulties in capturing complex variety–environment interactions. This study proposes a scalable graph-based collaborative filtering framework for assessing suitable planting locations for hybrid maize varieties, reformulating adaptability evaluation as a ranking task. The framework integrates two key innovations: a multi-layer perceptron (MLP) encoding module that leverages 15 variety trait features and 24 environmental variables to address the cold-start problem, enabling recommendations for previously unseen varieties; and a BPR+ loss function that distinguishes unsuitable environments from merely unplanted ones to accommodate the unique sparsity structure of agricultural trial data. Evaluated against six state-of-the-art graph-based recommendation models on a dataset of 189 locations and 1,102 hybrid maize varieties across China's major agro-ecological zones, our method consistently outperforms existing approaches, achieving approximately 5% improvement over the strongest baseline. The MLP module attains cold-start NDCG above 0.8, and BPR+ yields significant gains in Recall@1–4 and NDCG@1–5. A case study of the widely cultivated variety ZD958, validated against both national promotion data (2019–2024) and multi-year field-trial yields from 40 locations (2017–2024), confirms that the model's rank-ordered recommendations align closely with independent yield evidence: top-ranked locations achieve both the highest productivity and the greatest yield stability. This framework provides an AI-based decision support tool for variety placement optimization and climate-resilient agriculture.","摘要 提高作物品种适应性评价的精度，对于在气候变化背景下最大化产量和保障粮食安全至关重要。依赖专家知识或过程模型的传统方法面临可扩展性挑战，且难以捕捉复杂的品种–环境互作。本研究提出了一种可扩展的基于图协同过滤框架，用于评估杂交玉米品种的适宜种植地点，将适应性评价重新表述为排序任务。该框架融合了两项关键创新：一是多层感知机（MLP）编码模块，利用15个品种性状特征和24个环境变量来缓解冷启动问题，从而能够对未见过的品种进行推荐；二是BPR+损失函数，将不适宜环境与仅未种植环境区分开来，以适应农业试验数据特有的稀疏结构。在中国主要农业生态区189个地点和1，102个杂交玉米品种的数据集上，与六种最先进的基于图的推荐模型进行对比评估，我们的方法始终优于现有方法，相较最强基线提升约5%。MLP模块的冷启动NDCG达到0.8以上，BPR+在Recall@1–4和NDCG@1–5上均带来显著提升。对广泛种植品种ZD958的案例研究，结合国家推广数据（2019–2024年）和40个地点多年田间试验产量数据（2017–2024年）进行验证，证实模型的排序推荐与独立产量证据高度一致：排名靠前的地点既具有最高生产力，也具有最大产量稳定性。该框架为品种布局优化和气候韧性农业提供了基于人工智能的决策支持工具。","in silico Plants","2026-09-20T00:00:00Z",79,{"impact":17,"substance":60,"depth":17,"authority":19,"freshness":63,"relevant":20,"comment":174},"提出基于图协同过滤的杂交玉米品种适宜种植区推荐框架，覆盖全国189个地点、1102个品种，冷启动与排序指标均有提升，对品种布局优化有实用价值。",[176],{"name":170,"url":167},[25,26,178,29,179],"种业振兴","品种布局",[181,182],"杂交玉米 品种 适宜种植区","图协同过滤 玉米 推荐","杂交玉米品种适宜种植区-3199","10.1093\u002Finsilicoplants\u002Fdiag026",{"doi":184,"openalex_id":186,"authors":187,"venue":170,"cited_by_count":34,"oa_url":167,"card":217,"direction":107,"ingested_from":45},"W7213886755",[188,191,194,197,199,202,205,208,211,214],{"name":189,"orcid":190},"Yanyun Han","https:\u002F\u002Forcid.org\u002F0000-0003-4333-8565",{"name":192,"orcid":193},"Zhongqiang Liu","https:\u002F\u002Forcid.org\u002F0009-0008-4290-8718",{"name":195,"orcid":196},"Aiwen Wang","https:\u002F\u002Forcid.org\u002F0000-0003-0031-5857",{"name":198,"orcid":8},"Yong Zhang",{"name":200,"orcid":201},"Shouhui Pan","https:\u002F\u002Forcid.org\u002F0000-0003-4917-9921",{"name":203,"orcid":204},"Xiangyu Zhao","https:\u002F\u002Forcid.org\u002F0000-0001-7668-1509",{"name":206,"orcid":207},"Qiusi Zhang","https:\u002F\u002Forcid.org\u002F0000-0001-5469-1802",{"name":209,"orcid":210},"Qi Zhang","https:\u002F\u002Forcid.org\u002F0009-0004-5061-5422",{"name":212,"orcid":213},"Xinglin Piao","https:\u002F\u002Forcid.org\u002F0000-0003-3774-5789",{"name":215,"orcid":216},"Kaiyi Wang","https:\u002F\u002Forcid.org\u002F0009-0000-5365-7178",{"tldr":218,"method":219,"finding":220,"direction":107,"opportunity":221},"提出图协同过滤框架，将玉米品种适种区评估转化为排序任务，推荐适宜种植地点。","图协同过滤+MLP编码15个品种性状与24个环境变量，BPR+损失，189地点1","方法优于六个基线约5%，冷启动NDCG超0.8，推荐排名与独立产量证据高度一致。","可拓展至多作物多品种跨区域迁移推荐，并融合气候情景预测未来适种区变化。","2026-09-22T23:30:49.262877Z",{"id":224,"title":225,"url":226,"summary":227,"summary_zh":228,"content":8,"source_name":229,"source_url":226,"published_at":230,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":231,"score_detail":232,"sources":235,"tags":237,"search_phrases":240,"slug":243,"view_count":34,"doi":244,"paper":245,"created_at":255},3197,"YieldVision: Model-Driven Decision Support System for Crop Yield Prediction","https:\u002F\u002Fdoi.org\u002F10.56201\u002Frjpst.vol.9.no1.2026.pg176.194","Agriculture, a cornerstone of global food security, faces unprecedented challenges in achieving sustainable productivity. Crop yield prediction, a crucial aspect of agricultural planning, suffers from inefficiencies rooted in technological and methodological gaps. While traditional approaches rely on historical data and empirical estimations, they often fail to address the complexity of environmental interactions because they lack real-time data integration and limited access to accurate data, leading to inaccuracies and resource mismanagement. Addressing these issues requires the development of advanced Machine Learning (ML) models-based system that can predict crop yield with high accuracy in dynamic environments. This paper presents YieldVision, a decision support system driven by advanced ML models for Crop Yield Prediction based on environmental, soil, and crop-specific factors. In this work, we proposed three crop prediction models: Random Forest (RF), Long Short Time Memory (LSTM) and Hybrid (RF LSTM). RF regressor effectively captured the complex, nonlinear relationships among soil nutrients, fertilizer application, and temperature. The LSTM network learned the way short-term weather fluctuations impact crop development over time, and the hybrid model combined LSTM for environmental-related factors, and RF for soil-related factors. Experimental results on the Crop Yield Prediction Dataset containing soil and weather parameters showed that RF has the highest accuracy 𝑅2 = 99.10% and outperforms both LSTM and hybrid which have 87.50% and 97.30% accuracy respectively, confirming its suitability for the dataset. RF has the lowest RMSE and MAE compared to LSTM and hybrid models. The models are integrated into a decision support system that is deployed to operationalize the models in real-world settings with user interfaces accessible via mobile or web-delivered real-time alerts and yield forecasts to farmers and agronomists.","农业作为全球粮食安全的基石，在实现可持续生产力方面面临着前所未有的挑战。作物产量预测作为农业规划的关键环节，因技术和方法上的不足而效率低下。传统方法依赖历史数据和经验估算，但由于缺乏实时数据集成且难以获取准确数据，往往无法应对环境相互作用的复杂性，导致预测不准确和资源管理不善。解决这些问题需要开发基于先进机器学习（ML）模型的系统，能够在动态环境中高精度地预测作物产量。本文提出了YieldVision，一个由先进机器学习模型驱动的决策支持系统，用于基于环境、土壤和作物特定因素的作物产量预测。在本研究中，我们提出了三种作物预测模型：随机森林（RF）、长短期记忆网络（LSTM）和混合模型（RF-LSTM）。随机森林回归器有效捕捉了土壤养分、施肥量和温度之间复杂的非线性关系。LSTM网络学习了短期天气波动如何随时间影响作物发育，而混合模型将LSTM用于环境相关因素，RF用于土壤相关因素。在包含土壤和天气参数的作物产量预测数据集上的实验结果表明，随机森林具有最高的准确率𝑅2 = 99.10%，优于LSTM和混合模型，后两者的准确率分别为87.50%和97.30%，证实了随机森林对该数据集的适用性。与LSTM和混合模型相比，随机森林具有最低的RMSE和MAE。这些模型被集成到一个决策支持系统中，该系统已部署用于在实际环境中运行这些模型，其用户界面可通过移动端或网页访问，向农民和农艺师提供实时警报和产量预测。","RESEARCH JOURNAL OF PURE SCIENCE AND TECHNOLOGY","2026-09-21T00:00:00Z",68,{"impact":16,"substance":233,"depth":18,"authority":16,"freshness":63,"relevant":20,"comment":234},20,"提出RF、LSTM及混合模型并集成决策支持系统，方法对比与精度数据扎实，对智慧农业产量预测有参考价值，但属单篇论文且来源期刊影响力有限。",[236],{"name":229,"url":226},[25,26,27,238,239],"机器学习","决策支持系统",[241,242],"YieldVision 作物产量预测","RF LSTM 混合模型 产量预测","YieldVision作物产量预测-3197","10.56201\u002Frjpst.vol.9.no1.2026.pg176.194",{"doi":244,"openalex_id":246,"authors":247,"venue":229,"cited_by_count":34,"oa_url":8,"card":250,"direction":107,"ingested_from":45},"W7213883348",[248],{"name":249,"orcid":8},"D.J.S. Sako",{"tldr":251,"method":252,"finding":253,"direction":107,"opportunity":254},"提出YieldVision决策支持系统，用RF、LSTM及混合模型预测作物产量并部署应用。","基于土壤、环境与作物数据，构建RF、LSTM和RF-LSTM混合模型并集成到We","随机森林表现最佳，R²达99.10%，优于LSTM（87.50%）和混合模型（97.30%）。","可探索多源实时数据融合与模型可解释性，提升跨区域泛化能力并降低对高精度历史数据的依赖。","2026-09-22T23:30:43.591303Z",{"id":257,"title":258,"url":259,"summary":260,"summary_zh":261,"content":8,"source_name":262,"source_url":259,"published_at":230,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":263,"score_detail":264,"sources":266,"tags":268,"search_phrases":270,"slug":273,"view_count":34,"doi":274,"paper":275,"created_at":293},3177,"A comparative analysis on maize yield prediction using sentinel 2A and Landsat 8 satellite image in Sundarganj, Gaibandha, Bangladesh","https:\u002F\u002Fdoi.org\u002F10.3329\u002Fbjar.v51i1.92530","Maize is an important cereal crops in Bangladesh. Over the last two decades, its cultivation has increased promisingly, especially in the Northern part of Bangladesh. The effective estimation of crop yields at a regional scale holds significant importance in facilitating decision-making within the agricultural sector, thereby ensuring grain security. The traditional ground-based measurement techniques suffer from inefficiencies, and there exists a need for a reliable, precise, and effective method for estimating regional crop yields. This study used remote sensing (RS) techniques for forecasting pre-harvest maize yield to improve the management system. Currently, the normalized difference vegetation index (NDVI) is widely used to predict crop yield including maize. However, the present study used Landsat 8 (~ 30 m) and Sentinel 2A (~ 10 m) high resolution data for 2018-2019 and 2019-2020 to predict maize yield based on the year 2020-2021 at Sundarganj Upazila in Gaibandha district. The single cloud free image acquisition date based on maximum NDVI for both satellite images was used for each maize growing period to develop a yield prediction model. A regression model was performed between NDVI values and 20 farmers field-level maize yields. The absolute mean error of prediction was about 10.30% for Landsat 8 and 6.70% for Sentinel 2A compared to the actual maize yield during 2020-2021. The study revealed that NDVI data extracted from Sentinel 2A high resolution satellite images can be successfully used to predict the maize yield with appreciable accuracy. Finally, this study has demonstrated the efficacy of combining multi-temporal remote sensing data for accurate maize yield estimation, aiding agricultural authorities and production enterprises in the timely formulation and refinement of cropping strategies and management policies for the ongoing season. Bangladesh J. Agril. Res. 51(1): 501-521, March 2026","玉米是孟加拉国重要的谷类作物。过去二十年间，其种植面积增长显著，尤其是在孟加拉国北部地区。在区域尺度上有效估算作物产量对于促进农业部门决策、进而保障粮食安全具有重要意义。传统的地面测量技术效率低下，亟需一种可靠、精确且有效的区域作物产量估算方法。本研究采用遥感（RS）技术预测收获前玉米产量，以改进管理体系。目前，归一化植被指数（NDVI）被广泛用于预测包括玉米在内的作物产量。然而，本研究利用Landsat 8（约30 m）和Sentinel 2A（约10 m）高分辨率数据，基于2018—2019年和2019—2020年的数据，对盖班达县孙达尔甘杰乌帕齐拉2020—2021年的玉米产量进行预测。在每个玉米生长期，选取两颗卫星影像中NDVI最大值对应的单幅无云影像获取日期，用于建立产量预测模型。对NDVI值与20户农民田块级玉米产量进行回归建模。与2020—2021年实际玉米产量相比，Landsat 8的绝对平均预测误差约为10.30%，Sentinel 2A约为6.70%。研究表明，利用Sentinel 2A高分辨率卫星影像提取的NDVI数据可成功用于预测玉米产量，且精度令人满意。最后，本研究证明了结合多时相遥感数据进行准确玉米产量估算的有效性，有助于农业主管部门和生产企业在当季及时制定和完善种植策略与管理政策。Bangladesh J. Agril. Res. 51(1): 501-521, March 2026","Bangladesh Journal of Agricultural Research",65,{"impact":63,"substance":233,"depth":18,"authority":19,"freshness":63,"relevant":20,"comment":265},"基于Sentinel 2A与Landsat 8的玉米遥感估产对比研究，方法清晰、误差数据具体，对遥感估产有参考价值，但属区域小尺度研究，公共影响有限。",[267],{"name":262,"url":259},[25,27,29,124,269],"NDVI",[271,272],"Sentinel 2A Landsat 8 玉米产量预测","孟加拉国 Sundarganj 玉米遥感估产","Sentinel2ALandsat8玉米产量预测-3177","10.3329\u002Fbjar.v51i1.92530",{"doi":274,"openalex_id":276,"authors":277,"venue":262,"cited_by_count":34,"oa_url":259,"card":288,"direction":161,"ingested_from":45},"W7213918280",[278,280,282,284,286],{"name":279,"orcid":8},"N Mohammad",{"name":281,"orcid":8},"MA Islam",{"name":283,"orcid":8},"MG Mahboob",{"name":285,"orcid":8},"MM Rahman",{"name":287,"orcid":8},"I Ahmed",{"tldr":289,"method":290,"finding":291,"direction":161,"opportunity":292},"用Sentinel 2A与Landsat 8的NDVI回归模型预测孟加拉国玉米产量并比较精度。","基于最大NDVI单期影像与20个农户地块产量做回归，比较两种卫星。","Sentinel 2A预测绝对平均误差6.70%，优于Landsat 8的10.30%。","可探索多时相NDVI与机器学习融合，提升小农户尺度玉米估产精度与迁移性。","2026-09-22T23:30:23.551878Z",{"id":295,"title":296,"url":297,"summary":298,"summary_zh":299,"content":8,"source_name":116,"source_url":297,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":118,"score_detail":300,"sources":302,"tags":304,"search_phrases":307,"slug":310,"view_count":34,"doi":311,"paper":312,"created_at":336},3128,"Spatiotemporal synchronization-based seed position estimation for multi-row precision planting","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112396","Spatiotemporal synchronization-based seed position estimation for multi-row precision planting。Computers and Electronics in Agriculture","基于时空同步的多行精量播种种子位置估计。《农业计算机与电子》",{"impact":17,"substance":233,"depth":17,"authority":62,"freshness":12,"relevant":20,"comment":301},"核心期刊论文，提出基于时空同步的多行精量播种种子位置估计方法，对智能播种机具研发有参考价值，但属细分技术进展，公共影响有限。",[303],{"name":116,"url":297},[25,26,305,29,306],"智能农机","精准播种",[308,309],"多行精准播种 种子位置估计","时空同步 播种监测","多行精准播种种子位置估计-3128","10.1016\u002Fj.compag.2026.112396",{"doi":311,"openalex_id":313,"authors":314,"venue":116,"cited_by_count":34,"oa_url":8,"card":331,"direction":44,"ingested_from":45},"W7213976849",[315,318,320,323,325,327,329],{"name":316,"orcid":317},"Lin Jia","https:\u002F\u002Forcid.org\u002F0009-0002-2422-7974",{"name":319,"orcid":8},"Qingjie Wang",{"name":321,"orcid":322},"Hongwen Li","https:\u002F\u002Forcid.org\u002F0000-0002-5536-2346",{"name":324,"orcid":8},"Chao Wang",{"name":326,"orcid":8},"Jin He",{"name":328,"orcid":8},"Caiyun Lu",{"name":330,"orcid":8},"Xinyue Zhang",{"tldr":332,"method":333,"finding":334,"direction":44,"opportunity":335},"提出基于时空同步的多行精量播种种子位置估计方法。","利用播种机多行作业的时空同步信号估计种子落点位置。","该方法能实现多行精量播种的种子位置估计，提升播种质量监测精度。","可结合机器视觉与GNSS实时校正，发展播种质量在线评估与变量播种闭环控制。","2026-09-22T23:30:01.680027Z"]