An Efficient Method for Estimating Wheat Heading Dates Using UAV Images

Remote Sensing

Wei Guo¹  Haozhou Wang¹  Jian Wang²  Cong Wang³  Yun Shi³  Yulin Duan³  Wenbin Wu³  Licheng Zhao³ 

[1] Institute for Sustainable Agro-Ecosystem Services, Graduate School of Agricultural and Life Science, The University of Tokyo, Tokyo 188-0002, Japan;Institute of Cotton Research, Shanxi Agricultural University, Yuncheng 044000, China;Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of Agriculture and Rural Affairs, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China;

关键词: heading date;    UAV images;    plant height;    growth curve;    wheat;   

DOI  :  10.3390/rs13163067

来源: DOAJ

【 摘 要 】

Convenient, efficient, and high-throughput estimation of wheat heading dates is of great significance in plant sciences and agricultural research. However, documenting heading dates is time-consuming, labor-intensive, and subjective on a large-scale field. To overcome these challenges, model- and image-based approaches are used to estimate heading dates. Phenology models usually require complicated parameters calibrations, making it difficult to model other varieties and different locations, while in situ field-image recognition usually requires the deployment of a large amount of observational equipment, which is expensive. Therefore, in this study, we proposed a growth curve-based method for estimating wheat heading dates. The method first generates a height-based continuous growth curve based on five time-series unmanned aerial vehicle (UAV) images captured over the entire wheat growth cycle (>200 d). Then estimate the heading date by generated growth curve. As a result, the proposed method had a mean absolute error of 2.81 d and a root mean square error of 3.49 d for 72 wheat plots composed of different varieties and densities sown on different dates. Thus, the proposed method is straightforward, efficient, and affordable and meets the high-throughput estimation requirements of large-scale fields and underdeveloped areas.

【 授权许可】

Unknown