Hyperspectral Inversion of Chlorophyll Content in Maize Leaves Based on Image and Spectrum Fusion

Wang, Xiaoxia; Xiao, Zhiyun

doi:10.1109/cac57257.2022.10055240

Cited by 5 publications

(6 citation statements)

References 4 publications

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“…The UHD-185 hyperspectral imager was used to collect hyperspectral images of bayberry under natural light conditions in the field. The UHD-185 hyperspectral imager has a spectral resolution of 4 nm, a sampling interval of less than 100 μs, and a spatial resolution of 1mm within the wavelength range of 454 nm to 998 nm [ 17 ]. Hyperspectral images were collected using the Spectralon ® (North Sutton, NH, USA) standard diffuse reflectance whiteboard before measurement to calibrate the hyperspectral images and a tripod was used to allow the UHD-185 to capture the hyperspectral images of bayberry in a fixed position.…”

Section: Acquisition and Preprocessing Of Datamentioning

confidence: 99%

Non-Destructive Determination of Bayberry Sugar and Acidity by Hyperspectral Remote Sensing of Si-Sensor and Low-Cost Portable Instrument Development

Wang,

Wu,

Tian

et al. 2023

Sensors

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The digitalization of information is crucial for the upgrading of the bayberry digital agriculture industry, while the low-cost information detection sensing equipment for bayberry are a bottleneck for the digital development of the industry. The existing rapid and non-destructive detection devices for fruit acidity and sugar content mainly use near-infrared and mid-infrared spectral characteristic for detection. These devices use expensive InGaAs sensor, which are difficult to promote and apply in the bayberry digital industry. This study is based on the high-spectral range of 454–998 nm in bayberry fruit to study the mechanism of fruit sugar and acidity content detection and to develop a portable bayberry fruit sugar and acidity detection device using Si-sensor in order to achieve low-cost quality parameter detection of bayberry fruit. The research results show that: Based on the hyperspectral of bayberry fruit, the sensitive wavelength for sugar content inversion is 610 nm, and the inversion accuracy (RMSE) is 1.399Brix; the sensitive wavelength for pH inversion is 570 nm, and the inversion accuracy (RMSE) is 0.1329. Based on the above spectroscopic detection mechanism and spectral dimension reduction methods, combined with low-cost Si-sensor (400–1000 nm), a low-cost non-destructive portable bayberry fruit sugar and acidity detection device has been developed, with detection accuracies of 94.74% and 97.14%, respectively. This bayberry fruit sugar and acidity detector provides a low-cost portable non-destructive quality detection instrument of bayberry, which is in line with the industrial group of low consumption in which the bayberry is mainly cultivated on a small scale, accelerating the digitalization process of the bayberry industry.

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Section: Acquisition and Preprocessing Of Datamentioning

confidence: 99%

Non-Destructive Determination of Bayberry Sugar and Acidity by Hyperspectral Remote Sensing of Si-Sensor and Low-Cost Portable Instrument Development

Wang,

Wu,

Tian

et al. 2023

Sensors

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“…2 measurement of plant water stress induced by water deficit. However, this method is labourintensive, time-consuming, and only provides point measurements (Wang, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…They can automatically learn the relationship between the reflectance spectrum and the desired information while being robust against the noise and uncertainties in spectral and ground truth measurements (Giannoni et al, 2018). Wang (2022) did an inversion of nitrogen and chlorophyll content in crop leaves based on hyperspectral and Partial Least Squares Regression (PLSR). Ridge Regression Models was used to estimate grain yield from field spectral data in bread wheat (Triticum Aestivum L.) grown under three water regimes (Hernandez et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Use of HSI Coupled with Machine Learning for the Identification of Water Stress in Pear Seedling Leaves

An,

Wang

2024

Preprint

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Water plays a vital role in the healthy growth of pears. Early detection of water stress can play a significant role in the timely management of water deficiency for pear yield. Most current methods are labour-intensive, time-consuming, only provide point measurements, and could be destructive. In this research, a real-time non-destructive method using a push-broom hyperspectral system (400-1000nm) was used to collect hyperspectral image data and detect the water stress of the pear seedling leaves. To build a reliable prediction model, machine learning techniques were used. The Successive Projections Algorithm (SPA) was applied for optimal wavelength selection. In particular, CNN was applied to obtain the features of key wavelengths. Both the CNN features and key wavelengths were put into RR-MLR, BLR and ENN for analysis. The training accuracy of the three modellings all reach the accuracy above 70% after about 100 epochs, while combination of CNN features outperformed the mere main spectra analysis. This research demonstrated that hyperspectral imaging coupled with machine learning techniques could be applied to predict the water content of pear leaves predict non-destructively.

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“…Wang et al employed random forest (RF) techniques to identify specific wavelengths in the spectrum that are sensitive to chlorophyll content in rice canopy leaves. Then, they developed a partial least squares regression model to estimate the chlorophyll content of maize leaves [13]. Ban et al established an estimation model of LCC based on rice chlorophyll content using partial least squares regression (PLSR), support vector regression (SVR), and artificial neural network (ANN) methods [14].…”

Section: Introductionmentioning

confidence: 99%

Potato Leaf Chlorophyll Content Estimation through Radiative Transfer Modeling and Active Learning

Ma,

Qiu,

Zhang

et al. 2023

Agronomy

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Leaf chlorophyll content (LCC) significantly correlates with crop growth conditions, nitrogen content, yield, etc. It is a crucial indicator for elucidating the senescence process of plants and can reflect their growth and nutrition status. This study was carried out based on a potato nitrogen and potassium fertilizer gradient experiment in the year 2022 at Keshan Farm, Qiqihar Branch of Heilongjiang Agricultural Reclamation Bureau. Leaf hyperspectral and leaf chlorophyll content data were collected at the potato tuber formation, tuber growth, and starch accumulation periods. The PROSPECT-4 radiative transfer model was employed to construct a look-up table (LUT) as a simulated data set. This was accomplished by simulating potato leaves’ spectral reflectance and chlorophyll content. Then, the active learning (AL) technique was used to select the most enlightening training samples from the LUT based on the measured potato data. The Gaussian process regression (GPR) algorithm was finally employed to construct the inversion models for the chlorophyll content of potato leaves for both the whole and single growth periods based on the training samples selected by the AL method and the ground measured data of the potatoes. The R2 values of model validation accuracy for the potato whole plantation period and three single growth periods are 0.742, 0.683, 0.828, and 0.533, respectively with RMSE values of 4.207, 4.364, 2.301, and 3.791 µg/cm2. Compared with the LCC inversion accuracy through LUT with a cost function, the validation accuracies of the GPR_PROSPECT-AL hybrid model were improved by 0.119, 0.200, 0.328, and 0.255, and the RMSE were reduced by 3.763, 2.759, 0.118, and 5.058 µg/cm2, respectively. The study results indicate that the hybrid method combined with the radiative transfer model and active learning can effectively select informative training samples from a data pool and improve the accuracy of potato LCC estimation, which provides a valid tool for accurately monitoring crop growth and growth health.

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Hyperspectral Inversion of Chlorophyll Content in Maize Leaves Based on Image and Spectrum Fusion

Cited by 5 publications

References 4 publications

Non-Destructive Determination of Bayberry Sugar and Acidity by Hyperspectral Remote Sensing of Si-Sensor and Low-Cost Portable Instrument Development

Non-Destructive Determination of Bayberry Sugar and Acidity by Hyperspectral Remote Sensing of Si-Sensor and Low-Cost Portable Instrument Development

Use of HSI Coupled with Machine Learning for the Identification of Water Stress in Pear Seedling Leaves

Potato Leaf Chlorophyll Content Estimation through Radiative Transfer Modeling and Active Learning

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