An Empirical Assessment of Angular Dependency for RedEdge-M in Sloped Terrain Viticulture

Gong, Chizhang; Buddenbaum, Henning; Retzlaff, Rebecca; Udelhoven, Thomas

doi:10.3390/rs11212561

Cited by 6 publications

(2 citation statements)

References 29 publications

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“…RedEdge is a 5-discrete-narrowband frame multispectral sensor that is commonly used in remote-sensing studies and precision agriculture [74][75][76][77][78]. It is considered stable and reliable, and as an improved product of hyperspectral technology, its costs have reduced drastically [25,26], thus broadening its applicability in tea quality monitoring.…”

Section: Ground Multispectral Imagesmentioning

confidence: 99%

“…It is considered stable and reliable, and as an improved product of hyperspectral technology, its costs have reduced drastically [25,26], thus broadening its applicability in tea quality monitoring. Previous studies have typically integrated RedEdge-mx in unmanned aerial vehicles [74,75,77,78], which means more complex data acquisition steps, lower spatial resolution, and more data processing procedures. Close-range applications are limited [76]; however, the ground portable handheld method proposed in this study can obtain reliable multi-spectral data more easily and accurately compared to application with unmanned aerial vehicles.…”

Section: Ground Multispectral Imagesmentioning

confidence: 99%

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Application of Multispectral Camera in Monitoring the Quality Parameters of Fresh Tea Leaves

Chen

Zhao

et al. 2021

Remote Sensing

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The production of high-quality tea by Camellia sinensis (L.) O. Ktze is the goal pursued by both producers and consumers. Rapid, nondestructive, and low-cost monitoring methods for monitoring tea quality could improve the tea quality and the economic benefits associated with tea. This research explored the possibility of monitoring tea leaf quality from multi-spectral images. Threshold segmentation and manual sampling methods were used to eliminate the image background, after which the spectral features were constructed. Based on this, the texture features of the multi-spectral images of the tea canopy were extracted. Three machine learning methods, partial least squares regression, support vector machine regression, and random forest regression (RFR), were used to construct and train multiple monitoring models. Further, the four key quality parameters of tea polyphenols, total sugars, free amino acids, and caffeine content were estimated using these models. Finally, the effects of automatic and manual image background removal methods, different regression methods, and texture features on the model accuracies were compared. The results showed that the spectral characteristics of the canopy of fresh tea leaves were significantly correlated with the tea quality parameters (r ≥ 0.462). Among the sampling methods, the EXG_Ostu sampling method was best for prediction, whereas, among the models, RFR was the best fitted modeling algorithm for three of four quality parameters. The R2 and root-mean-square error values of the built model were 0.85 and 0.16, respectively. In addition, the texture features extracted from the canopy image improved the prediction accuracy of most models. This research confirms the modeling application of a combination of multi-spectral images and chemometrics, as a low-cost, fast, reliable, and nondestructive quality control method, which can effectively monitor the quality of fresh tea leaves. This provides a scientific reference for the research and development of portable tea quality monitoring equipment that has general applicability in the future.

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