数字农科院2.0

Determining rapeseed lodging angles and types for lodging phenotyping using morphological traits derived from UAV images

文献类型： 外文期刊

作者： Chufeng Wang;Shijie Xu;Chenghai Yang;Yunhao You;Jian Zhang;Jie Kuai;Jing Xie;Qingsong Zuo;Mingli Yan;Hai Du;Ni Ma;Bin Liu;Liangzhi You;Tao Wang;Hao Wu

作者机构：

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

期刊名称： European Journal of Agronomy

ISSN： 1161-0301

年卷期： 2024 年 155 卷

页码：

收录情况： SCIE（2024版）

摘要： Crop lodging detrimentally affects crop yield and mechanical harvest efficiency. Traditional remote sensing-based 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° for TLA and 7.4° 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.

分类号：

  • 相关文献

[1]Advancing lodging resistance in maize: Integrating genetic, hormonal, and agronomic insights for sustainable crop productivity. Shumila Ishfaq,Yi Ding,Xiaoyan Liang,Wei Guo. 2025

[2]Numerical Simulation And Analysis On S.pray Drift Movement Of M ultirotor Plant Protection Unmanned Aerial Vehicle. Yang, Fengbo,Sun, Zhu,Zhou, Qingqing,Cai, Chen,Xue, Xinyu,Yang, Fengbo. 2018

[3]Droplet deposition and control effect of insecticides sprayed with an unmanned aerial vehicle against plant hoppers. Qin, Wei-Cai,Qiu, Bai-Jing,Qin, Wei-Cai,Xue, Xin-Yu,Chen, Chen,Xu, Zhu-Feng,Zhou, Qing-Qing.

[4]Comparison Of Spray Deposition, Control E.fficacy On Wheat Aphids A nd Working Efficiency In The Wheat Field Of The Unmanned Aerial Vehicle With Boom Sprayer And Two Conventional Knapsack Sprayers. Lan, Yubin,Wang, Guobin,Yuan, Huizhu,Ouyang, Fan,Chen, Pengchao,Han, Yuxing,Qi, Haixia. 2019

[5]Estimation Of Land Surface Temperature F.rom Unmanned Aerial Vehicle L oaded Thermal Imager Data. Si, M, Tang, BH, Li, ZL. 2018

[6]Estimating maize seedling number with UAV RGB images and advanced image processing methods. Liu, Shuaibing,Yin, Dameng,Feng, Haikuan,Li, Zhenhai,Xu, Xiaobin,Shi, Lei,Jin, Xiuliang. 2022

[7]Control effect on cotton aphids of insecticides sprayed with unmanned aerial vehicles under different flight heights and spray volumes. Hongyan Hu, Xiangliang Ren, Xiaoyan Ma, Huanhuan Li, Yajie Ma,Dan Wang, Xianpeng Song, Yanhua Meng, Yan Ma. 2021

[8]Estimation of Maize LAI Using Ensemble Learning and UAV Multispectral Imagery under Different Water and Fertilizer Treatments. Cheng, Qian,Xu, Honggang,Fei, Shuaipeng,Li, Zongpeng,Chen, Zhen. 2022

[9]Comparison of Different Dimensional Spectral Indices for Estimating Nitrogen Content of Potato Plants over Multiple Growth Periods. Fan Y.,Feng H.,Yue J.,Liu Y.,Jin X.,Xu X.,Song X.,Ma Y.,Yang G.. 2023

[10]Detecting spatial variability of soil compaction using soil apparent electrical conductivity and maize traits. Ren, Lidong,D'Hose, Tommy,Borra-Serrano, Irene,Lootens, Peter,Hanssens, Daan,De Smedt, Philippe,Cornelis, Wim M.,Ruysschaert, Greet. 2022

[11]Faba Bean (Vicia faba L.) Yield Estimation Based on Dual-Sensor Data. Cui, Yuxing,Ji, Yishan,Liu, Rong,Li, Weiyu,Liu, Yujiao,Liu, Zehao,Zong, Xuxiao,Yang, Tao. 2023

[12]Above-Ground Biomass Estimation And Yield Prediction In Potato By Using Uav-Based Rgb And Hyperspectral Imaging. Li, B, Xu, XM, Zhang, L, Han, JW, Bian, CS, Li, GC, Liu, JG, Jin, LP. 2020

[13]Deep convolutional neural networks for estimating maize above-ground biomass using multi-source UAV images: a comparison with traditional machine learning algorithms. Yu, Danyang,Zha, Yuanyuan,Sun, Zhigang,Li, Jing,Jin, Xiuliang,Zhu, Wanxue,Bian, Jiang,Ma, Li,Zeng, Yijian,Su, Zhongbo. 2022

[14]Dynamic Harvest Index Estimation of Winter Wheat Based on UAV Hyperspectral Remote Sensing Considering Crop Aboveground Biomass Change and the Grain Filling Process. Jianqiang Ren,Ningdan Zhang,Xingren Liu,Shangrong Wu,Dandan Li. 2022

[15]UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat. Fei, Shuaipeng,Hassan, Muhammad Adeel,Xiao, Yonggui,Su, Xin,Chen, Zhen,Cheng, Qian,Duan, Fuyi,Chen, Riqiang,Ma, Yuntao. 2022

[16]Exploring Multisource Feature Fusion and Stacking Ensemble Learning for Accurate Estimation of Maize Chlorophyll Content Using Unmanned Aerial Vehicle Remote Sensing. Weiguang Zhai,Changchun Li,Qian Cheng,Fan Ding,Zhen Chen. 2023

[17]Automatic counting of rapeseed inflorescences using deep learning method and UAV RGB imagery. Li J.,Li Y.,Qiao J.,Li L.,Wang X.,Yao J.,Liao G.. 2023

[18]Radiometric Correction of Multispectral Field Images Captured under Changing Ambient Light Conditions and Applications in Crop Monitoring. Beibei Xue,Bo Ming,Jiangfeng Xin,Hongye Yang,Shang Gao,Huirong Guo,Dayun Feng,Chenwei Nie,Keru Wang,Shaokun Li. 2023

[19]Wheat Yield Prediction Using Machine Learning Method Based on UAV Remote Sensing Data. Shurong Yang,Lei Li,Shuaipeng Fei,Mengjiao Yang,Zhiqiang Tao,Yaxiong Meng,Yonggui Xiao. 2024

[20]Using an optimized texture index to monitor the nitrogen content of potato plants over multiple growth stages. Yiguang Fan,Haikuan Feng,Jibo Yue,Xiuliang Jin,Yang Liu,Riqiang Chen,Mingbo Bian,Yanpeng Ma,Xiaoyu Song,Guijun Yang. 2023

作者其他论文 更多>>