文献类型： 外文期刊

作者： Wang, Chufeng ¹ ; Xu, Shijie ¹ ; Yang, Chenghai ³ ; You, Yunhao ¹ ; Zhang, Jian ¹ ; Kuai, Jie ⁴ ; Xie, Jing ⁵ ; Zuo, Qingsong ⁶ ; Yan, Mingli ⁷ ; Du, Hai ⁸ ; Ma, Ni ⁹ ; Liu, Bin ¹ ; You, Liangzhi ¹ ; Wang, Tao ¹¹ ; Wu, Hao ¹² ;

作者机构： 1.Huazhong Agr Univ, Macro Agr Res Inst, Coll Resource & Environm, 1 Shizishan St, Wuhan 430070, Peoples R China

2.Lower Reaches Minist Agr, Key Lab Farmland Conservat Middle, Wuhan 430070, Peoples R China

3.USDA Agr Res Serv, Aerial Applicat Technol Res Unit, College Stn, TX 77845 USA

4.Huazhong Agr Univ, Coll Plant Sci & Technol, Wuhan 430070, Peoples R China

5.Huazhong Agr Univ, Coll Engn, Wuhan 430070, Peoples R China

6.Yangzhou Univ, Jiangsu Key Lab Crop Genet & Physiol, Yangzhou 225009, Peoples R China

7.Hunan Acad Agr Sci, Crop Res Inst, Changsha 410125, Peoples R China

8.Southwest Univ, Coll Agron & Biotechnol, Chongqing Engn Res Ctr Rapeseed, Chongqing 400716, Peoples R China

9.Chinese Acad Agr Sci, Minist Agr & Rural Affairs, Oil Crops Res Inst, Key Lab Crop Physiol & Prod,Key Lab Biol & Genet I, Wuhan 430062, Peoples R China

10.Int Food Policy Res Inst, Washington, DC USA

11.Guizhou Acad Agr Sci, Guishou Rapeseed Inst, Guiyang 550008, Peoples R China

12.Cent China Normal Univ, Coll Urban & Environm Sci, 152 Luoyu Rd, Wuhan 430079, Peoples R China

关键词： Crop lodging; Total lodging angle; Root lodging angle; Lodging types; Unmanned aerial vehicle

期刊名称：EUROPEAN JOURNAL OF AGRONOMY （ 影响因子：5.2； 五年影响因子：5.9 ）

ISSN： 1161-0301

年卷期： 2024 年 155 卷

页码：

收录情况： SCI

摘要： Crop lodging detrimentally affects crop yield and mechanical harvest efficiency. Traditional remote sensingbased methods primarily focus on the identification and area extraction of lodging using image texture and spectrum. However, the response of image texture and spectrum to lodging is indirect and varies under diverse conditions. Moreover, other important finer details of lodging phenotyping, such as lodging angle and lodging type, have frequently been neglected. In this study, a robust and accurate method was developed for investigating lodging phenotypes in the field. The method was based on the three-dimensional morphological information of rapeseed (Brassica napus L.) canopy reconstructed from unmanned aerial vehicle (UAV) images. In contrast to traditional remote sensing methods that only identify lodging targets and their respective areas, the novel method in this study calculated the total lodging angle (TLA), root lodging angle (RLA), stem lodging angle (SLA = TLA - RLA), and lodging types according to a morphological method and a lodging classification model. Initially, the method employed a geometric model to characterize the stalk shape of lodged rapeseed. After assessing numerous lodging samples from individual rapeseed plants, the circle function was identified as the optimal geometric model. With this optimal function, the canopy height derived from the UAV images was found effective in calculating TLA, RLA, and SLA across 24 rapeseed cultivars in five climatic zones within the Yangtze River Basin (YRB) in China. Results showed that the average root mean square error (RMSE) was 8.3 degrees for TLA and 7.4 degrees for RLA. Subsequently, based on field measured data of SLA and RLA, a decision tree model was constructed to classify lodging types and an accuracy of 95.4% was achieved. Using the classification model and estimated values of RLA and SLA, the spatial distribution information and specific area estimates for different lodging types were obtained. Based on the analysis of these results, the rapeseed cultivars Zhongshuang 11 and Dadi 199 were determined to be the dominant cultivars with lodging resistance in the YRB, even though they did not achieve the mean high yields in multiple climatic zones. However, the lodging -prone cultivars such as Qinyou7 and Qinyou33 fell under the low -yield level in all climatic zones. The robust and cost-effective method proposed in this study for acquiring detailed crop lodging phenotyping data has the potential to enhance mechanized harvesting, accurately estimate the risk of low yield, and assess the lodging status of various crops.