Mapping Soybean Maturity and Biochemical Traits Using UAV-Based Hyperspectral Images

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 19; p. 4807
• Main Authors: Wang, Lizhi, Gao, Rui, Li, Changchun, Wang, Jian, Liu, Yang, Hu, Jingyu, Li, Bing, Qiao, Hongbo, Feng, Haikuan, Yue, Jibo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2023
• Subjects: Algorithms; Altitude; Chlorophyll; Crops; Cultivars; Cultivation; Data assimilation; Deep learning; Digital cameras; Digital mapping; Estimation; Evaluation; Farms; Flavonoids; Gaussian process; Hyperspectral imaging; Image quality; leaf chlorophyll content; Least squares method; Leaves; Machine learning; Maturity; Moisture content; Monitoring; Nitrogen balance; nitrogen balance index; Normalized difference vegetative index; Parameters; Physicochemical properties; Plant breeding; Raw materials; Regression; Remote sensing; Reproductive organs; Root-mean-square errors; Solar radiation; Soybean; Soybeans; Time series; Unmanned aerial vehicles; Vegetation; Water content; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15194807

Soybeans are rich in high-quality protein and raw materials for producing hundreds of chemical products. Consequently, soybean cultivation has gained widespread prevalence across diverse geographic regions. Soybean breeding necessitates the development of early-, standard-, and late-maturing cultivars to accommodate cultivation at various latitudes, thereby optimizing the utilization of solar radiation. In the practical process of determining the maturity of soybean breeding materials within the breeding field, the ripeness is assessed based on three critical criteria: pod moisture content, leaf color, and the degree of leaf shedding. These parameters reflect the crown structure, physicochemical parameters, and reproductive organ changes in soybeans during the maturation process. Therefore, methods for analyzing soybean maturity at the breeding plot scale should match the standards of agricultural experts to the maximum possible extent. This study presents a hyperspectral remote sensing approach for monitoring soybean maturity. We collected five periods of unmanned aerial vehicle (UAV)-based soybean canopy hyperspectral digital orthophoto maps (DOMs) and ground-level measurements of leaf chlorophyll content (LCC), flavonoids (Flav), and the nitrogen balance index (NBI) from a breeding farm. This study explores the following aspects: (1) the correlations between soybean LCC, NBI, Flav, and maturity; (2) the estimation of soybean LCC, NBI, and Flav using Gaussian process regression (GPR), partial least squares regression (PLSR), and random forest (RF) regression techniques; and (3) the application of threshold-based methods in conjunction with normalized difference vegetation index (NDVI)+LCC and NDVI+NBI for soybean maturity monitoring. The results of this study indicate the following: (1) Soybean LCC, NBI, and Flav are associated with maturity. LCC increases during the beginning bloom period (P1) to the beginning seed period (P3) and sharply decreases during the beginning maturity period (P4) stage. Flav continues to increase from P1 to P4. NBI remains relatively consistent from P1 to P3 and then drops rapidly during the P4 stage. (2) The GPR, PLSR, and RF methodologies yield comparable accuracy in estimating soybean LCC (coefficient of determination (R2): 0.737–0.832, root mean square error (RMSE): 3.35–4.202 Dualex readings), Flav (R2: 0.321–0.461, RMSE: 0.13–0.145 Dualex readings), and NBI (R2: 0.758–0.797, RMSE: 2.922–3.229 Dualex readings). (3) The combination of the threshold method with NDVI < 0.55 and NBI < 8.2 achieves the highest classification accuracy (accuracy = 0.934). Further experiments should explore the relationships between crop NDVI, the Chlorophyll Index, LCC, Flav, and NBI and crop maturity for different crops and ecological areas.