文献类型： 外文期刊

作者： Zhang, Baoyuan ¹ ; Gu, Limin ¹ ; Dai, Menglei ¹ ; Bao, Xiaoyuan ¹ ; Sun, Qian ² ; Zhang, Mingzheng ² ; Qu, Xuzhou ² ; Li, Zhenhai ⁵ ; Zhen, Wenchao ¹ ; Gu, Xiaohe ² ;

作者机构： 1.Hebei Agr Univ, Coll Agron, Baoding 071001, Hebei, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Res Ctr Informat Technol, Beijing 100089, Peoples R China

3.State Key Lab North China Crop Improvement & Regul, Baoding 071001, Hebei, Peoples R China

4.Minist Agr & Rural Affairs, Key Lab North China Water Saving Agr, Baoding 071001, Hebei, Peoples R China

5.Shandong Univ Sci & Technol, Coll Geodesy & Geomat, Qingdao 266590, Shandong, Peoples R China

关键词： Grain filling rate; UAV; Winter wheat; Vegetation index

期刊名称：FIELD CROPS RESEARCH （ 影响因子：5.8； 五年影响因子：6.9 ）

ISSN： 0378-4290

年卷期： 2024 年 306 卷

页码：

收录情况： SCI

摘要： The grain filling rate (GFR) plays a pivotal role in assessing winter wheat grain development and guiding crop variety selection and cultivation strategies. Presently, GFR monitoring primarily relies on destructive sampling methods, which are impractical for swift assessments in extensive agricultural fields. Our study introduces a novel approach for estimating winter wheat grain filling rate (GFR) using leaf chlorophyll content (LCC) and Leaf Area Index (LAI) extracted from UAV imagery. Initially, we established a quantitative relationship between GFR and LAI as well as LCC in winter wheat. Subsequently, UAV multispectral imagery was employed to track the time-series dynamics of LAI and LCC during the grain-filling stage. Ultimately, we constructed an estimation model for winter wheat GFR and achieved plot-scale GFR mapping through UAV-based imaging technology. To identify sensitive vegetation index combinations for LAI and chlorophyll content (SPAD), we employed adaptive re-weighted sampling and continuous projection algorithms, respectively. Following this, we employed partial least squares and random forest algorithms to develop LAI and SPAD estimation models, which were then compared to full-band regression. Finally, GFR estimation was based on the conversion models of LAI and SPAD to GFR. Our findings revealed that, despite variations in water supply, there were no significant differences in GFR trends among the irrigated treatments. However, in the nitrogenous fertilizer application treatments, GFR initially declined with increased nitrogenous fertilizer supply, reaching its peak between 21 and 25 days post flowering, during which period the differences in GFR among the various nitrogenous fertilizer application treatments were not significant. As the reproductive period advanced, higher nitrogenous fertilizer treatments maintained a higher GFR for a more extended period and experienced a slower decline compared to the lower nitrogenous fertilizer treatments. LAI validation yielded an R2 of 0.84, a root mean square error (RMSE) of 0.39, and a normalized root mean square error (NRMSE) of 10.9%. SPAD validation resulted in R2, RMSE, and NRMSE values of 0.79%, 1.30%, and 12.5%, respectively. The GFR estimation achieved an R2 of 0.77, an RMSE of 0.32 mg/d, and an NRMSE of 11.3%. Mapping the GFR distribution in the field, coupled with water and fertilizer management, supports UAVs in monitoring crop growth and refining field-level water and fertilizer decisions.