Trait-Specific Contributions of UAV Multispectral, RGB and Structural Features to Soybean SPAD and Plant Height Phenotyping

Abstract

Unmanned aerial vehicle (UAV) imagery can support plot-scale crop phenotyping, but spectral, RGB and structural predictors may contribute differently to different traits. We compared six predefined feature groups for predicting soybean SPAD and plant height (PH) in a 1.3 ha field experiment in Sanya, China. The field contained 6197 soybean planting plots, of which 234 had paired SPAD and PH measurements. Multispectral bands, vegetation indices (VIs), RGB descriptors and digital surface model (DSM) metrics were extracted from DJI Mavic 3 Multispectral imagery. Six regression algorithms were evaluated using random fivefold cross-validation, spatial block cross-validation and nested spatial cross-validation. Under random cross-validation, ExtraTrees with multispectral bands, VIs and RGB descriptors produced the numerically highest SPAD performance (R2 = 0.589; RMSE = 6.66), while BayesianRidge with multispectral bands, VIs and DSM metrics produced the highest PH performance (R2 = 0.760; RMSE = 7.14 cm). Nested spatial cross-validation yielded R2 = 0.473 and RMSE = 7.56 for SPAD and R2 = 0.690 and RMSE = 8.13 cm for PH. G4 was selected in four of the five outer folds for SPAD, although the selected algorithm varied, and G5 was selected in all five outer folds for PH. VIs improved prediction of both traits relative to the original bands. Adding RGB descriptors produced only a small and model-dependent improvement for SPAD, whereas adding DSM metrics produced a larger and more consistent improvement for PH. The complete feature set did not outperform G4 for SPAD or G5 for PH. The retained models were applied to all 6197 plots to map SPAD, PH and their field relative combinations. Because all of the validations used one field and one UAV acquisition date, the results describe performance within this experiment and do not establish transferability to other sites, years or growth stages.

Plant phenotyping relevance

UAVマルチスペクトル・RGB・構造特徴からSPADと草丈を推定する特徴抽出および回帰手法を、複数の空間交差検証で比較・評価しており、植物表現型取得が研究の中心である。

Code and data availability

The supplied blocks describe UAV soybean phenotyping data and analysis but contain no public dataset, image, code, or model deposit. Supplementary materials (Tables S1–S2, Figure S4) are referenced without any availability URL, and no data or code availability statement appears in the provided text.

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