Estimating potato aboveground biomass using unmanned aerial vehicle RGB imagery and analyzing its relationship with tuber biomass

Abstract

Monitoring the aboveground biomass (AGB) is critical for assessing crop growth status, predicting yield, and making informed crop management decisions. This study aimed to develop an efficient and robust model for predicting potato AGB using data derived from unmanned aerial vehicle (UAV) RGB imagery, and to clarify the relationship between AGB and tuber biomass (TB). Remote sensing images of the potato canopy at multiple growth stages were acquired over two consecutive years (2022–2023), together with synchronous ground-based AGB and TB measurements. Sixty-four candidate variables encompassing spectral, color, structure, and texture features were extracted from the 2022 RGB images. We identified five single variables most sensitive to AGB through correlation analysis, which were then subjected to linear, polynomial, logarithmic, exponential, and power regressions. Recursive feature elimination (RFE) and variance inflation factor (VIF) analyses were used to select multivariate combinations as input parameters for Partial Least Squares (PLS) and Random Forest (RF) models. The optimal single-variable and multivariate regression models were selected based on the Bayesian information criterion (BIC), and subsequently applied to predict AGB in field trial plots for 2023. Additionally, we analyzed the dynamic relationship between AGB and TB, as well as the effects of genotype and nitrogen management on the accuracy of AGB predictions and its relationship with TB. The results showed that: (1) Structural indicators had the highest correlation with AGB among the four features. The linear regression using canopy volume (CVol) as an input parameter (Model 1) exhibited superior performance among the single-variable regression models (R² = 0.75, RMSE = 0.42 kg m⁻², BIC = −272.92). Meanwhile, the RF regression model with canopy cover (CC), maximum canopy height (CHₘₐₓ), and average canopy height (CHₘₑₐₙ) as input parameters (Model 2) had the lowest BIC value of −314.15 (R² = 0.82, RMSE = 0.36 kg m⁻²), and its predicted values for the new dataset were significantly correlated with the measured AGB values (correlation coefficient of 0.84). Furthermore, Model 2 showed a stronger predictive power for AGB in plots with the high-erectability genotype ('Zhongshu18', R² = 0.78, RMSE = 1.02 kg m⁻²) or those treated with ammonium nitrogen (NH₄⁺-N) (R² = 0.75, RMSE = 1.24 kg m⁻²). (2) A significant positive correlation was observed between TB and cumulative AGB, with R² values of 0.77 in 2022 and 0.76 in 2023 (p < 0.01). When the data were analyzed separately by genotype, the linear correlation between TB and cumulative AGB was further enhanced. Moreover, when analyzed by nitrogen form, the correlation under nitrate nitrogen and control treatments was significantly stronger than under NH₄⁺-N treatments. In conclusion, our results suggest that canopy structure indicators incorporating both horizontal and vertical dimensions, when combined with the RF algorithm, have the greatest potential for predicting AGB, and that the cumulative AGB over time can effectively estimate potato TB, although crop genotype and nitrogen management can affect the estimation accuracy of AGB as well as biomass allocation patterns. This study provides an efficient and cost-effective approach to accurately estimate the AGB of potato crops in the field, which is valuable for predicting yields of root and tuber crops and optimizing agronomic management practices.

Plant phenotyping relevance

UAV RGB画像からジャガイモの地上部バイオマスを推定する画像解析・回帰モデルを開発し、別年の圃場データで予測性能を検証しており、表現型取得手法が研究の中心である。

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