Monitoring the leaf equivalent water thickness of kiwifruit in high temperature using leaf spectral reflectance

Zhang, Zhen; Fu, Yu; Li, Hualong; Guo, Jianping; Pan, Yuying; Zhang, Yong; Zhang, Weimin; Wang, Jinghong; Liu, Yuefeng; Liu, Lu

doi:10.1080/00387010.2022.2149558

Cited by 5 publications

(2 citation statements)

References 48 publications

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“…This method requires the selection of suitable empirical vegetation indices tailored to specific plant types to achieve enhanced estimation accuracy. As an illustration, Zhang et al [16] computed recently developed spectral indices to estimate the equivalent water thickness (EWT) of kiwifruit leaves. Their findings demonstrated that the newly proposed three-band vegetation index (R2039-R2438)/R752 exhibited superior performance in estimating EWT for kiwifruit leaves, boasting an R 2 value of 0.771, a root mean square error of 0.0024 g•cm −2 , and a residual prediction deviation of 2.09.…”

Section: Introductionmentioning

confidence: 99%

Inversion of Leaf Water Content of Cinnamomum camphora Based on Preferred Spectral Index and Machine Learning Algorithm

Yang,

Zhang,

et al. 2023

Forests

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Plant leaf water content significantly influences photosynthetic efficiency and crop yield. Leaf water content (LWC) and equivalent water thickness (EWT) are indicators that reflect the water state within plant tissues, and they play a crucial role in assessing plant water supply and usage. In recent years, there has been a growing focus on the rapid and precise determination of plant water content. In this study, Cinnamomum camphora (C. camphora) was chosen as the subject of investigation. After acquiring spectral data, three types of vegetation indices were computed: the empirical vegetation index, the random combination dual-band vegetation index, and the ‘trilateral’ parameter. Four groups of optimal spectral index screening strategies were established, namely an empirical vegetation index group (G1), a random combination dual-band vegetation index group (G2), a ‘trilateral’ parameter group (G3), and a mixed group (G4). Three algorithms, specifically random forest (RF), radial basis function neural network (RBFNN), and support vector machine (SVM), were employed for the estimation of leaf water content (LWC) and equivalent water thickness (EWT) in mature C. camphora. The results demonstrated that the G4 group displayed superior performance, yielding five optimal spectral indices for LWC: water index (WI), optimized soil-adjusted vegetation index (OSAVI), difference vegetation index (DVI) at wavelengths 734 and 956 nm, first-order difference vegetation index (DVI-FD) at wavelengths 1009 and 774 nm, and red-edge amplitude (Dr). With regard to EWT estimation, the five optimal spectral indices encompassed the red-edge normalized difference vegetation index (RE-NDVI), simple ratio water index (SRWI), difference vegetation index (DVI) at wavelengths 700 and 1167 nm, first-order difference vegetation index (DVI-FD) at wavelengths 1182 and 1514 nm, and red-edge area (SDr). Utilizing these indices as inputs significantly enhanced the accuracy of the models, with the RF model emerging as the most effective for estimating LWC and EWT in C. camphora. Based on the LWC estimation model of the G4 group and the RF algorithm, the determination coefficient (R2) for both the training and test sets reached 0.848 and 0.871, respectively. The root mean square error (RMSE) was 0.568% for the training set and 0.582% for the test set, while the average relative error (MRE) stood at 0.806% and 0.642%, respectively. Regarding the EWT estimation model, R2 values of 0.887 and 0.919 were achieved for the training and test sets, accompanied by RMSE values of 0.6 × 10−3 g·cm−2 and 0.7 × 10−3 g·cm−2, and MRE values of 3.198% and 2.901%, respectively. These findings lay a solid foundation for hyperspectral moisture monitoring in C. camphora and offer valuable reference for the rapid assessment of crop growth status.

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Section: Introductionmentioning

confidence: 99%

Inversion of Leaf Water Content of Cinnamomum camphora Based on Preferred Spectral Index and Machine Learning Algorithm

Yang,

Zhang,

et al. 2023

Forests

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“…6,7 Four water absorption bands near 980, 1200, 1450, and 1940 nm are attributed to the selective absorption of leaf water. [8][9][10] The selective absorptions of biochemical components in leaves and the multiple reection processes of leaf cells determine the solar spectral reection characteristics of leaves. According to the formation mechanism of leaf reection characteristics, some camouage materials have been developed to resist hyperspectral detection in the solar spectral region.…”

Section: Introductionmentioning

confidence: 99%