Nondestructive estimation of leaf chlorophyll content in banana based on unmanned aerial vehicle hyperspectral images using image feature combination methods
IntroductionNondestructive quantification of leaf chlorophyll content (LCC) of banana and its spatial distribution across growth stages from remotely sensed data provide an effective avenue to diagnose nutritional deficiency and guide management practices. Unmanned aerial vehicle (UAV) hyperspectral...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1536177/full |
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| Summary: | IntroductionNondestructive quantification of leaf chlorophyll content (LCC) of banana and its spatial distribution across growth stages from remotely sensed data provide an effective avenue to diagnose nutritional deficiency and guide management practices. Unmanned aerial vehicle (UAV) hyperspectral imagery can document abundant texture features (TFs) and spectral information in a field experiment due to the high spatial and spectral resolutions. However, the benefits of using the fine spatial resolution accessible from UAV data for estimating LCC for banana have not been adequately quantified.MethodsIn this study, two types of image features including vegetation indices (VIs) and TFs extracted from the first-three-principal-component-analyzed images (TFs-PC1, TFs-PC2, and TFs-PC3) were employed. We proposed two methods of image feature combination for banana LCC inversion, which are a two-pair feature combination and a multivariable feature combination based on four machine learning algorithms (MLRAs).ResultsThe results indicated that compared to conventionally used VIs alone, the banana LCC estimations with both proposed VI and TF combination methods were all significantly improved. Comprehensive analyses of the linear relationships between all constructed two-pair feature combinations and LCC indicated that the ratio of mean to modified red-edge sample ratio index (MEA/MSRre) stood out (R2 = 0.745, RMSE = 2.17). For multivariable feature combinations, four MLRAs using original or two selected VIs and TFs-PC1 combination groups resulted in better LCC estimation than the other input variables. We concluded that the nonlinear Gaussian process regression model with the VIs and TFs-PC1 combination selected by maximal information coefficient as input achieved the highest accuracy in LCC prediction for banana, with the highest R2 of 0.776 and lowest RMSE of 2.04. This study highlights the potential of the proposed image feature combination method for deriving high-resolution maps of banana LCC fundamental for precise nutritional diagnosing and operational agriculture management. |
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| ISSN: | 1664-462X |