Evaluation of Field Germination of Soybean Breeding Crops Using Multispectral Data from UAV

Abstract

The use of multispectral aerial photography data contributes to the study of soybean plants by obtaining objective data. The evaluation of field germination of soybean crops was carried out using multispectral data (MSD). The purpose of this study was to develop ranges of field germination of soybean plants according to multispectral survey data from an unmanned aerial vehicle (UAV) for three years (2020, 2021, and 2022). As part of the ground-based research, the number of plants that sprang up per unit area was calculated and expressed as a percentage of the seeds sown. A DJI Matrice 200 Series v2 unmanned aerial vehicle and a MicaSense Altum multispectral camera were used for multispectral aerial photography. The correlation between ground-based and multispectral data was 0.70–0.75. The ranges of field germination of soybean breeding crops, as well as the vegetation indices (VIs) normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and chlorophyll index green (ClGreen) were calculated according to Sturges’ rule. The accuracy of the obtained ranges was estimated using the mean absolute percentage error (MAPE). The MAPE values did not exceed 10% for the ranges of the NDVI and ClGreen vegetation indices, and were no more than 18% for the NDRE index. The final values of the MAPE for the three years did not exceed 10%. The developed software for the automatic evaluation of the germination of soybean crops contributed to the assessment of the germination level of soybean breeding crops using multispectral aerial photography data. The software considers data of the three vegetation indices and calculated ranges, and creates an overview layer to visualize the germination level of the breeding plots. The developed method contributes to the determination of field germination for numerous breeding plots and speeds up the process of breeding new varieties.

New Trends and Challenges in Precision and Digital Agriculture

“…These features make drones increasingly applicable to agriculture. Assessing the field germination of soybean plants [71]; determining the nutritional status of spring wheat after the earing stage [72]; and monitoring nitrogen concentrations in walnut [73], cotton [74], or phenotyping in corn breeding [75] are just a few examples of what can be achieved using UAVs and spectral cameras in modern agriculture.…”

Hyperspectral reflectance and agro-physiological traits for field identification of salt-tolerant wheat genotypes using the genotype by yield*trait biplot technique

“…Furthermore, these biometrical techniques for identifying stress-tolerant genotypes rely on STIs. For example, GT, GYT, and genotype by yield*STI (GYSI) were employed to select barley drought-tolerant genotypes ( Bakhshi and Shahmoradi, 2023 ), wheat breeds for stress tolerance ( Mohammadi, 2019 ; Msundi et al., 2021 ; Zulfiqar et al., 2021 ), and other crops such as rice breeds for drought ( Sabouri et al., 2022 ), barley ( Kendal, 2020 ; Bakhshi and Shahmoradi, 2023 ), common bean genotypes ( Sofi et al., 2022 ), and soybean ( Kurbanov et al., 2023 ). Moreover, the GT model with SRIs in corn treated with nitrogen levels to identify trait relationships was used ( Santana et al., 2021 ).…”