Identification of the Initial Anthesis of Soybean Varieties Based on UAV Multispectral Time-Series Images

Author:

Pan Di12,Li Changchun1,Yang Guijun23,Ren Pengting2,Ma Yuanyuan2,Chen Weinan2,Feng Haikuan2ORCID,Chen Riqiang2,Chen Xin2,Li Heli2

Affiliation:

1. School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China

2. Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China

3. School of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, China

Abstract

Accurate and high-throughput identification of the initial anthesis of soybean varieties is important for the breeding and screening of high-quality soybean cultivars in field trials. The objectives of this study were to identify the initial day of anthesis (IADAS) of soybean varieties based on remote sensing multispectral time-series images acquired by unmanned aerial vehicles (UAVs), and analyze the differences in the initial anthesis of the same soybean varieties between two different climatic regions, Shijiazhuang (SJZ) and Xuzhou (XZ). First, the temporal dynamics of several key crop growth indicators and spectral indices were analyzed to find an effective indicator that favors the identification of IADAS, including leaf area index (LAI), above-ground biomass (AGB), canopy height (CH), normalized-difference vegetation index (NDVI), red edge chlorophyll index (CIred edge), green normalized-difference vegetation index (GNDVI), enhanced vegetation index (EVI), two-band enhanced vegetation index (EVI2) and normalized-difference red-edge index (NDRE). Next, this study compared several functions, like the symmetric gauss function (SGF), asymmetric gauss function (AGF), double logistic function (DLF), and fourier function (FF), for time-series curve fitting, and then estimated the IADAS of soybean varieties with the first-order derivative maximal feature (FDmax) of the CIred edge phenology curves. The relative thresholds of the CIred edge curves were also used to estimate IADAS, in two ways: a single threshold for all of the soybean varieties, and three different relative thresholds for early, middle, and late anthesis varieties, respectively. Finally, this study presented the variations in the IADAS of the same soybean varieties between two different climatic regions and discussed the probable causal factors. The results showed that CIred edge was more suitable for soybean IADAS identification compared with the other investigated indicators because it had no saturation during the whole crop lifespan. Compared with DLF, AGF and FF, SGF provided a better fitting of the CIred edge time-series curves without overfitting problems, although the coefficient of determination (R2) and root mean square error (RMSE) were not the best. The FDmax of the SGF-fitted CIred edge curve (SGF_CIred edge) provided good estimates of the IADAS, with an RMSE and mean average error (MAE) of 3.79 days and 3.00 days, respectively. The SGF-fitted_CIred edge curve can be used to group the soybean varieties into early, middle and late groups. Additionally, the accuracy of the IADAS was improved (RMSE = 3.69 days and MAE = 3.09 days) by using three different relative thresholds (i.e., RT50, RT55, RT60) for the three flowering groups compared to when using a single threshold (RT50). In addition, it was found that the IADAS of the same soybean varieties varied greatly when planted in two different climatic regions due to the genotype–environment interactions. Overall, this study demonstrated that the IADAS of soybean varieties can be identified efficiently and accurately based on UAV remote sensing multispectral time-series data.

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

Reference59 articles.

1. Soybean [Glycine max (L.) Merrill]: Importance as a crop and pedigree reconstruction of Korean varieties;Lee;Plant Breed. Biotechnol.,2015

2. An applied deep learning approach for estimating soybean relative maturity from UAV imagery to aid plant breeding decisions;Moeinizade;Mach. Learn. Appl.,2022

3. Toward a “green revolution” for soybean;Liu;Mol. Plant,2020

4. Field high-throughput phenotyping: The new crop breeding frontier;Araus;Trends Plant Sci.,2014

5. High-throughput estimation of crop traits: A review of ground and aerial phenotyping platforms;Jin;IEEE Geosci. Remote Sens. Mag.,2020

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