Study on the optimal algorithm prediction of corn leaf component information based on hyperspectral imaging

Wu, Qiong; Wang, Jihua; Wang, Cheng; Xu, Tongyu

doi:10.1016/j.infrared.2016.07.011

Cited by 12 publications

(9 citation statements)

References 16 publications

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“…In contrast, the genetic algorithm (GA) approach had a significant effect in the band optimization selection of the PLS model [17]. However, a simple GA algorithm implementation could only locate near-optimal solutions, while failing in most cases to converge on the optimal solution [60,61].…”

Section: Informative Bands Selected By Different Methods For Leaf Biomentioning

confidence: 99%

“…Unlike statistical significance-based variable selection used in stepwise selection, genetic algorithms (GA) are developed on the basis of biological evolution theory and natural selection [17,49], and have significant effects on the band selection of the PLS model [50][51][52]. Following [22], the main steps of GA-PLS include:…”

Section: Ga-plsmentioning

confidence: 99%

“…Due to the high collinearity or correlations among the reflectance values at different bands, one of the inherent challenges with the PLS approach, with so many wavelength variables, is the risk of overfitting [2]. It has been demonstrated in many previous studies, both experimentally and theoretically, that the performance of PLSR can be tremendously improved if only informative variables are included in the model [2,[16][17][18][19][20]. After those variables which contain irrelevant or redundant information are removed, the robustness of the calibration models can be enhanced [21].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Informative Bands Used in Different PLS Regressions for Estimating Leaf Biochemical Contents from Hyperspectral Reflectance

Jin

Wang

2019

Remote Sensing

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Partial least squares (PLS) regression models are widely applied in spectroscopy to estimate biochemical components through hyperspectral reflected information. To build PLS regression models based on informative spectral bands, rather than strongly collinear bands contained in the full spectrum, is essential for upholding the performance of models. Yet no consensus has ever been reached on how to select informative bands, even though many techniques have been proposed for estimating plant properties using the vast array of hyperspectral reflectance. In this study, we designed a series of virtual experiments by introducing a dummy variable (Cd) with convertible specific absorption coefficients (SAC) into the well-accepted leaf reflectance PROSPECT-4 model for evaluating popularly adopted informative bands selection techniques, including stepwise-PLS, genetic algorithms PLS (GA-PLS) and PLS with uninformative variable elimination (UVE-PLS). Such virtual experiments have clearly defined responsible wavelength regions related to the dummy input variable, providing objective criteria for model evaluation. Results indicated that although all three techniques examined may estimate leaf biochemical contents efficiently, in most cases the selected bands, unfortunately, did not exactly match known absorption features, casting doubts on their general applicability. The GA-PLS approach was comparatively more efficient at accurately locating the informative bands (with physical and biochemical mechanisms) for estimating leaf biochemical properties and is, therefore, recommended for further applications. Through this study, we have provided objective evaluations of the potential of PLS regressions, which should help to understand the pros and cons of PLS regression models for estimating vegetation biochemical parameters.

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Section: Informative Bands Selected By Different Methods For Leaf Biomentioning

confidence: 99%

Section: Ga-plsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of Informative Bands Used in Different PLS Regressions for Estimating Leaf Biochemical Contents from Hyperspectral Reflectance

Jin

Wang

2019

Remote Sensing

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“…Chlorophyll content has been assessed using spectral responses in several crops such as cucumber and corn, and Engenharia Agrícola, Jaboticabal, v.41, n.4, p.475-484, jul./aug. 2021 have generated good results (Liu et al, 2017;Wu et al, 2016;Xiaobo et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Predictive Models of Chlorophyll Content in Sugarcane Seedlings Using Spectral Images

Oliveros¹,

Tinini

Costa

et al. 2021

Eng. Agríc.

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Chlorophyll content is a widely used parameter for nutritional status diagnosis in sugarcane. This study aimed to develop a predictive model of chlorophyll content in sugarcane seedlings using spectral imagery analysis within the electromagnetic spectrum visible range. The experiment was carried out in a split-plot design, with two fertilization rates and three sugarcane cultivars. For chlorophyll analysis, 144 leaves were collected from seedlings. Chlorophyll contents were extracted and measured by SPAD-502 meter. Spectral images within the range of 480 to 710 nm were analyzed using reflectance, absorbance (white source), and fluorescence (source at 405 and 470 nm) responses. Predictive models were developed using multivariate regression methods such as Principal Component Regression and Partial Least Squares Regression. We chose the best model through absorbance response using variable selection and the PLSR method (R2P = 0.718 and RMSEP = 7.665). The wavelengths of 480, 490, 500, 600, 630, and 640 nm were identified as the best for total chlorophyll content determination. The spectral image processing-based method can provide a chlorophyll measurement equivalent to SPAD, with the advantage of having a higher spatial coverage over the entire leaf area. Besides, it can also support automation of the chlorophyll measurement in greenhouses.

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“…In theory, the specific property of a target object on each pixel can be predicted by using a proper remote sensing inversion model based on their spectral signatures. With the development of hyperspectral imaging technology, imaging spectrometers are widely used in agriculture-related processes, such as estimating chlorophyll [13] , water [14,15] , and nutrient [16,17] contents of crop leaves, monitoring damages caused by pests and diseases in farmland [18] , and examining disease spots on crop fruits and seeds [19,20] . Huang et al [21] quantified the disease index of yellow rust in wheat with photochemical reflectance index derived from aerial hyperspectral images.…”

Section: Introductionmentioning

confidence: 99%

Estimating the severity of apple mosaic disease with hyperspectral images

Ban

Tian

Chang

2019

International Journal of Agricultural and Biological Engineering

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Soil Plant Analysis Development (SPAD) Chlorophyll Meter reading was used to effectively characterize chlorophyll content, which is an important indicator of the health status of plant leaves. In this study, the hyperspectral images of apple leaves infected by apple mosaic virus (ApMV) were captured, and their SPAD values were measured. The spectral reflectance of leaves with varying degree infection of disease is significantly different. In particular, the reflectance in visible wavebands of leaves with a more serious infection was higher than that of leaves with a less severe infection. Several hyperspectral vegetation indices were highly correlated with the SPAD values of apple leaves (correlation coefficient > 0.9). Models were established to estimate apple foliar SPAD values based on these vegetation indices. Among the models, the multivariate regression model with partial least square regression (PLSR) method achieved the highest accuracy. The SPAD value of a whole apple leaf was calculated from its SPAD distribution image and used as a quantitative index to represent the health status of an apple leaf. Furthermore, the SPAD value of a whole apple leaf could also be estimated rapidly and accurately by extracting the spectral average value of the whole leaf using a simple model. It can be used as a rapid detection method of SPAD values of apple leaves to monitor and describe the health conditions of apple leaves quantitatively.

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Study on the optimal algorithm prediction of corn leaf component information based on hyperspectral imaging

Cited by 12 publications

References 16 publications

Evaluation of Informative Bands Used in Different PLS Regressions for Estimating Leaf Biochemical Contents from Hyperspectral Reflectance

Evaluation of Informative Bands Used in Different PLS Regressions for Estimating Leaf Biochemical Contents from Hyperspectral Reflectance

Predictive Models of Chlorophyll Content in Sugarcane Seedlings Using Spectral Images

Estimating the severity of apple mosaic disease with hyperspectral images

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