Estimation of Chlorophyll Content in Apple Leaves Based on Imaging Spectroscopy

Yu, Ruiyang; Zhu, Xujun; Cao, Shi; Xiong, Jingling; Wen, Xuezhi; Jiang, Yuanmao; Zhao, Gengxing

doi:10.1007/s10812-019-00841-1

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…Previous studies have found that wavelengths with correlation coefficients higher than 0.5 can be applied to model construction [27]. Based on this knowledge, this study proposed a Modified Correlation Coefficient Method (MCCM) to screen the sensitive wavelengths of nitrogen content in the hyperspectral images.…”

Section: Screening Of Sensitive Wavelengths Using Mccm Methodsmentioning

confidence: 99%

Retrieval of Nitrogen Content in Apple Canopy Based on Unmanned Aerial Vehicle Hyperspectral Images Using a Modified Correlation Coefficient Method

Zhu

et al. 2022

Sustainability

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The accurate retrieval of nitrogen content based on Unmanned Aerial Vehicle (UAV) hyperspectral images is limited due to uncertainties in determining the locations of nitrogen-sensitive wavelengths. This study developed a Modified Correlation Coefficient Method (MCCM) to select wavelengths sensitive to nitrogen content. The Normalized Difference Canopy Shadow Index (NDCSI) was applied to remove the shadows from UAV hyperspectral images, thus yielding the canopy spectral information. The MCCM was then used to screen the bands sensitive to nitrogen content and to construct spectral characteristic parameters. Finally, the optimal model for nitrogen content retrieval was established and selected. As a result, the screened sensitive wavelengths for nitrogen content selected were 470, 474, 490, 514, 582, 634, and 682 nm, respectively. Among the nitrogen content retrieval models, the best model was the Support Vector Machine (SVM) model. In the training set, this model outperformed the other models with an R2 of 0.733, RMSE of 6.00%, an nRMSE of 12.76%, and a MAE of 4.49%. Validated by the ground-measured nitrogen content, this model yielded good performance with an R2 of 0.671, an RMSE of 4.73%, an nRMSE of 14.83%, and a MAE of 3.98%. This study can provide a new method for vegetation nutrient content retrieval based on UAV hyperspectral data.

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Section: Screening Of Sensitive Wavelengths Using Mccm Methodsmentioning

confidence: 99%

Retrieval of Nitrogen Content in Apple Canopy Based on Unmanned Aerial Vehicle Hyperspectral Images Using a Modified Correlation Coefficient Method

Zhu

et al. 2022

Sustainability

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“…Most of the visible light region is absorbed and reflected by chloroplasts, which are most numerous in the palisade tissue, and this may have influenced the selection of key bands. In fact, Yu et al selected key bands in the visible light region to estimate the chlorophyll content in apple tree leaves [45]. In trees, TNCs include glucose, which is produced by photosynthesis, and starch, which is how numerous glucose molecules are stored [46].…”

Section: Comparison With Related Studiesmentioning

confidence: 99%

Estimation of the Total Nonstructural Carbohydrate Concentration in Apple Trees Using Hyperspectral Imaging

Kang,

Park,

Kim

et al. 2023

Horticulturae

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The total nonstructural carbohydrate (TNC) concentration is an important indicator of the growth period and health of fruit trees. Remote sensing can be applied to monitor the TNC concentration in crops in a non-destructive manner. In this study, hyperspectral imaging from an unmanned aerial vehicle was applied to estimate the TNC concentration in apple trees. Partial least-squares regression, ridge regression, and Gaussian process regression (GP) were used to develop estimation models, and their effectiveness using selected key bands as opposed to full bands was evaluated in an effort to reduce computational costs and improve reproducibility. Nine key bands were identified, and the GP-based model using these key bands performed almost as well as the models using full bands. These results can be combined with previous studies on estimating the nitrogen concentration to provide useful information for more precise nutrient management to improve the yield and quality of apple trees.

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Capabilities of Surface Enhanced Raman Spectroscopy for Identifying Multiple Pigments in a Complex Organic Mixture

Oleynik,

Kozhina,

Bedin

et al. 2023

Bull. Russ. Acad. Sci. Phys.

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The authors propose a way of identifying the composition of paints by means of surface enhanced Raman spectroscopy using signal-amplifying substrates with arrays of vertically standing silver nanowires. A model tempera paint based on egg white with inorganic pigments (red lead, massicot, and emerald green) is used to show that with a reduced concentration of pigment, substrates can greatly improve sensitivity when detecting pigments in lower concentrations up to 0.01 g, compared to signals from the Raman scattering of light on foil. Reinforcing substrates allows the sensitivity of the technique to be improved in order to accurately identify components used individually and in mixtures.

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Estimation of Chlorophyll Content in Apple Leaves Based on Imaging Spectroscopy

Cited by 5 publications

References 8 publications

Retrieval of Nitrogen Content in Apple Canopy Based on Unmanned Aerial Vehicle Hyperspectral Images Using a Modified Correlation Coefficient Method

Retrieval of Nitrogen Content in Apple Canopy Based on Unmanned Aerial Vehicle Hyperspectral Images Using a Modified Correlation Coefficient Method

Estimation of the Total Nonstructural Carbohydrate Concentration in Apple Trees Using Hyperspectral Imaging

Capabilities of Surface Enhanced Raman Spectroscopy for Identifying Multiple Pigments in a Complex Organic Mixture

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