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Therefore, timely monitoring of field crops is crucial in maintaining adequate nutrient and water levels in the crop fields. Vegetation indices are increasingly being studied for their use in plant nutrient management and in guiding fertilizer recommendations. However, these indices were not adequately evaluated for nutrient management of crops grown with different water and nitrogen application levels. This study investigated the potential application of RGB imagery from low-cost Unmanned Aerial Vehicles (UAVs), combined with machine learning techniques, for quantifying water and nutrient stress in sorghum [ Sorghum bicolor (L.) Moench] crop performance. The field experimental setup at Prairie View A&M University, located in Southeast Texas, applied four irrigation levels (0, 75, 100, and 125% of irrigation; the 100% irrigation level is a fully irrigated treatment with no water stress) and four nitrogen application rates (0, 90, 180, and 360 kg N ha −1 ). The RGB images were captured before harvesting the grain sorghum. Image segmentation for filtering sorghum crops from soil and weeds was performed using K-means clustering and a Convolutional Neural Network (CNN). The vegetation indices, including the Green-Red Vegetation Index (GRVI), Green Leaf Index (GLI), and Excess Green Index (ExG), derived from the segmented images were used to predict sorghum grain and biomass yields using multiple linear regression, Partial least squares regression, Ridge regression, and XGBoost. The results indicated that no single model consistently outperformed the others across both segmentation approaches and response variables, suggesting that the predictive information in the RGB indices is meaningful with selected ML approaches. For example, biomass prediction using ridge regression yielded a Pearson correlation coefficient (r) of 0.65 and an RMSE of 0.72 for K-means clustering-derived indices. The models exhibited moderate, stage-specific predictive performance, with no single regression method consistently outperforming the others across both segmentation approaches and response variables. Because imagery was acquired only once during the late reproductive period, the RGB indices primarily reflected canopy condition, maturity, and senescence at that time. The findings demonstrate the feasibility of a low-cost, single-date UAV RGB workflow for late-season sorghum assessment, while emphasizing the need for multi-temporal imagery and independent validation before application to season-long stress diagnosis or operational yield forecasting.","精准农业已成为在水资源与养分合理管理中的关键策略，以应对全球人口增长所驱动的日益增长的粮食需求。因此，及时监测田间作物对于维持作物田块中适宜的养分和水分水平至关重要。植被指数在植物养分管理和指导施肥推荐中的应用正日益受到研究。然而，这些指数尚未在不同水分和氮素施用水平下对作物养分管理进行充分评估。本研究探讨了来自低成本无人机（UAV）的RGB影像结合机器学习技术在量化高粱[Sorghum bicolor (L.) Moench]作物表现中水分和养分胁迫方面的潜在应用。位于德克萨斯州东南部的Prairie View A&M大学的田间试验设置了四个灌溉水平（灌溉量的0%、75%、100%和125%；100%灌溉水平为充分灌溉处理，无水分胁迫）和四个氮素施用量（0、90、180和360 kg N ha⁻¹）。RGB图像在收获籽粒高粱前采集。使用K-means聚类和卷积神经网络（CNN）进行图像分割，以将高粱作物与土壤和杂草分离。从分割图像中提取的植被指数，包括绿红植被指数（GRVI）、绿叶指数（GLI）和过量绿指数（ExG），被用于通过多元线性回归、偏最小二乘回归、岭回归和XGBoost预测高粱籽粒和生物量产量。结果表明，在两种分割方法和响应变量下，没有单一模型始终优于其他模型，这表明RGB指数中的预测信息在所选机器学习方法下具有意义。例如，使用岭回归进行生物量预测时，基于K-means聚类所得指数的Pearson相关系数（r）为0.65，RMSE为0.72。这些模型表现出中等、特定阶段的预测性能，在两种分割方法和响应变量下，没有单一回归方法始终优于其他方法。由于影像仅在生殖后期采集一次，RGB指数主要反映当时的冠层状况、成熟度和衰老程度。研究结果证明了低成本、","Frontiers in Environmental Science","2026-09-30T00:00:00Z","论文",false,{"impact":416,"substance":508,"depth":583,"authority":584,"freshness":173,"relevant":455,"comment":585},16,13,"低成本无人机RGB影像结合机器学习估算高粱生物量与产量的方法学研究，结论审慎，对精准农业养分水分管理有参考价值，但属单点单期试验，影响范围有限。",[587],{"name":578,"url":575},[589,590,591,592,593],"智慧农业","农业人工智能","精准农业","遥感","高粱",{"code":240,"slug":241,"name":242,"path":243,"parent_code":200},[596,597],{"code":96,"slug":97,"name":98,"path":99,"parent_code":72},{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":601},"classified","rules",{"candidates":602,"agricultural_evidence":605},[603,610,616,624,630,635,641,647],{"code":240,"score":604,"supported":605,"title":606,"abstract":608,"tags":609},15,true,[607],"sorghum",[593,607],[593],{"code":24,"score":604,"supported":605,"title":611,"abstract":613,"tags":615},[612],"machine learning",[614,612],"机器学习",[590],{"code":96,"score":617,"supported":605,"title":618,"abstract":619,"tags":623},9,[],[620,621,622],"施肥","养分管理","fertilizer",[],{"code":152,"score":617,"supported":605,"title":625,"abstract":626,"tags":629},[],[627,628,153],"灌溉","水资源",[],{"code":16,"score":173,"supported":605,"title":631,"abstract":632,"tags":634},[],[591,633],"precision agriculture",[589,591],{"code":144,"score":636,"supported":605,"title":637,"abstract":638,"tags":640},6,[],[639,145],"土壤",[],{"code":256,"score":642,"supported":605,"title":643,"abstract":644,"tags":646},3,[],[645],"间作",[],{"code":32,"score":455,"supported":581,"title":648,"abstract":649,"tags":650},[],[],[592],[652,653],"Prairie View A&M University 高粱 无人机","高粱 生物量 产量 无人机 RGB","PrairieViewAMUniversity高粱无人机-3918","10.3389\u002Ffenvs.2026.1928610",{"doi":655,"openalex_id":657,"authors":658,"venue":578,"cited_by_count":424,"oa_url":575,"card":673,"direction":679,"ingested_from":680},"W7214994063",[659,661,664,666,669,671],{"name":660,"orcid":11},"Anoop Valiya Veettil",{"name":662,"orcid":663},"Ripendra Awal","https:\u002F\u002Forcid.org\u002F0000-0002-2453-2592",{"name":665,"orcid":11},"Ali Fares",{"name":667,"orcid":668},"Binita Thapa","https:\u002F\u002Forcid.org\u002F0000-0001-7093-4915",{"name":670,"orcid":11},"Almoutaz Elhassan",{"name":672,"orcid":11},"Atikur Rahman",{"tldr":674,"method":675,"finding":676,"direction":677,"opportunity":678},"用无人机RGB影像与机器学习估算水分氮胁迫下高粱生物量和产量。","低成本无人机RGB影像，K-means与CNN分割，多种回归模型预测。","单一模型未持续最优，RGB指数在特定时期具中等预测能力。","农业遥感与作物表型","可研究多时相RGB影像与深度学习融合，提升胁迫下作物产量预测精度。","农业人工智能与决策模型","openalex","2026-10-01T23:30:54.379150Z",{"id":683,"title":684,"url":685,"summary":686,"summary_zh":687,"content":11,"source_name":688,"source_url":685,"published_at":689,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":690,"score_detail":691,"sources":693,"tags":695,"primary_topic":698,"secondary_topics":699,"classification":702,"search_phrases":722,"slug":725,"view_count":69,"doi":726,"paper":727,"created_at":737},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","2026-09-10T00:00:00Z",61,{"impact":416,"substance":361,"depth":604,"authority":385,"freshness":636,"relevant":455,"comment":692},"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。",[694],{"name":688,"url":685},[589,696,218,592,697],"精准施肥","NDVI",{"code":216,"slug":217,"name":218,"path":219,"parent_code":200},[700,701],{"code":96,"slug":97,"name":98,"path":99,"parent_code":72},{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":703},{"candidates":704,"agricultural_evidence":605},[705,709,713,718],{"code":216,"score":604,"supported":605,"title":706,"abstract":707,"tags":708},[217],[218,217],[218],{"code":96,"score":416,"supported":605,"title":710,"abstract":711,"tags":712},[622],[622],[696],{"code":32,"score":173,"supported":605,"title":714,"abstract":715,"tags":717},[],[592,716],"remote sensing",[697,592],{"code":16,"score":455,"supported":581,"title":719,"abstract":720,"tags":721},[],[],[589],[723,724],"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":726,"openalex_id":728,"authors":729,"venue":688,"cited_by_count":424,"oa_url":11,"card":732,"direction":677,"ingested_from":680},"W7212289620",[730],{"name":731,"orcid":11},"Mohammed A. Naser",{"tldr":733,"method":734,"finding":735,"direction":677,"opportunity":736},"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",{"technologies":739,"keywords":763},[740,742,743,745,747,749,751,752,753,755,757,759,761],{"name":590,"count":741},7,{"name":591,"count":741},{"name":744,"count":642},"农业物联网",{"name":746,"count":642},"遥感监测",{"name":748,"count":69},"农业机器人",{"name":750,"count":69},"农业遥感",{"name":614,"count":69},{"name":592,"count":69},{"name":754,"count":455},"作物表型",{"name":756,"count":455},"农产品溯源",{"name":758,"count":455},"分子育种",{"name":760,"count":455},"图像识别",{"name":762,"count":455},"物联网",[764,765,766,768,770,772,773,774,776,778,780,781],{"name":589,"count":141},{"name":696,"count":524},{"name":767,"count":741},"水肥一体化",{"name":769,"count":408},"小农户",{"name":771,"count":400},"变量施肥",{"name":218,"count":400},{"name":621,"count":642},{"name":775,"count":642},"农业减排",{"name":777,"count":642},"化肥减量",{"name":779,"count":642},"有机肥",{"name":210,"count":642},{"name":226,"count":642}]