Detecting macronutrients content and distribution in oilseed rape leaves based on hyperspectral imaging

Zhang, Xiaolei; Liu, Fei; He, Yong; Gong, Xian Zheng

doi:10.1016/j.biosystemseng.2013.02.007

Cited by 121 publications

(96 citation statements)

References 48 publications

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“…Our result in validation set is comparatively equivalent to Zhang et al [11] phosphorus prediction when they applied hyperspectral imaging technique in VIS-NIR region (380 -1030 nm) on oilseed rape leaves region of interest. Photosynthetic process largely relies on P containing compounds [65].…”

Section: Prediction Of Macronutrient Contentsupporting

confidence: 64%

“…P prediction was poor whereas authors in these studies have employed full spectral range (400 -1000 nm). Using Vis-NIR hyperspectral imaging, Zhang et al [11] gave acceptable prediction performance of leaf P macronutrient content in region of interest. Since correlations between polyphenol and mineral content in olive leaves were established in Cetinkaya et al [12] study, we might expect better prediction performance of leaf macronutrient content by using reflectance additionally with fluorescence data.…”

Section: Introductionmentioning

confidence: 99%

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Use of Fluorescence and Reflectance Spectra for Predicting Okra (<i>Abelmoschus esculentus</i>) Yield and Macronutrient Contents of Leaves

Dibi¹,

Bosson²,

Zobi³

et al. 2017

OJAppS

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In-field proximal sensing of most major crops nutrients still remains an economical and technical challenge. For this purpose, the use of effective multi-excitation fluorescence and reflectance wavelengths is explored in this work on Okra plant. Visible-near infrared (400 -1000 nm) reflectance and multi-fluorescence data were collected at leaf scale in a chemically fertilized field by using an USB spectrometer mounted with an Arduino-based LED driver clip. N, P, K and Ca content of samples leaves were measured using reference methods. Average pods yield and leaves macronutrients content were calibrated using IRIV-PLS regression after spectra pretreatments. Single informative wavelengths bands in reflectance, red and far-red fluorescences were selected for building yield and macronutrient content models. We showed that flowering stage was more suitable for yield prediction. Moderately useful macronutrient models were found in Ca content (RPD val = 1.93, r P = 0.818) and potassium content with RPD val = 1.8, r P = 0.88. P and N yielding prediction performance of RPD val = 1.61 (r P = 0.718 ) and RPD val = 1.46 (r P = 0.56) respectively were less accurate. This study demonstrates potentiality of fluorescence and reflectance spectroscopy for accurate estimation of leaf macronutrient content and crop yield. High selectivity obtained from resulted spectral bands could lead to the development of reliable, rapid and cost-effective devices for nutrient diagnosis.

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Section: Prediction Of Macronutrient Contentsupporting

confidence: 64%

Section: Introductionmentioning

confidence: 99%

Use of Fluorescence and Reflectance Spectra for Predicting Okra (<i>Abelmoschus esculentus</i>) Yield and Macronutrient Contents of Leaves

Dibi¹,

Bosson²,

Zobi³

et al. 2017

OJAppS

View full text Add to dashboard Cite

show abstract

“…This was probably due to the distinctly different canopy structural changes associated with the phenological development of the two crops, especially during the period from flowering to harvest (Fieuzal, Baup, & Marais-Sicre, 2013). The leaf is the major photosynthetic organ until full flowering (Diepenbrock, 2000;Zhang, Liu, He, & Gong, 2013). From the start of the flowering, there is a drastic decline in leaf area due to the shedding of leaves which leads to much reduced photosynthetically active area, and this in turn leads to the decline of leaf chlorophyll content (Edwards, 2011).…”

Section: Canolamentioning

confidence: 99%

Mapping spatial variability of crop growth conditions using RapidEye data in Northern Ontario, Canada

Shang

Liu

et al. 2015

Remote Sensing of Environment

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“…In addition, there were two obvious absorption peaks in the graph. The absorption peak at 680 nm is mainly caused by the absorption of chlorophyll on the surface of the fruit [21] which reflects the color information of the fruit surface, while the absorption peak near 960 nm is mainly caused by the water absorption [22]. …”

Section: Reflectance Spectra Of Pearsmentioning

confidence: 99%

Wavelength Selection for Detection of Slight Bruises on Pears Based on Hyperspectral Imaging

Hao

Zhang

et al. 2016

Applied Sciences

Self Cite

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Hyperspectral imaging technology was employed to detect slight bruises on Korla pears. The spectral data of 60 bruised samples and 60 normal samples were collected by a hyperspectral imaging system. To select the characteristic wavelengths for detection, several chemometrics methods were used on the raw spectra. Firstly, principal component analysis (PCA) was conducted on the spectra ranging from 420 to 1000 nm of all samples. Considering that the reliability of the first two PCs was more than 90%, five characteristic wavelengths (472, 544, 655, 688 and 967 nm) were selected by the loading plot of PC1 and PC2. Then, each of the wavelength variables was considered as an independent classifier for bruised/normal classification, and all classifiers were evaluated by the receiver operating characteristic (ROC) analysis. Two wavelengths (472 and 967 nm) with the highest values under the curve (0.992 and 0.980) were finally selected for modeling. The classifying model was built by partial least squares discriminant analysis (PLS-DA) and the bruised/normal classification accuracy of the modeling set (45 damaged samples and 45 normal samples) and prediction set (15 damaged samples and 15 normal samples) was 98.9% and 100%, respectively, which is similar to that of the PLS-DA model based on the whole spectral range. The result shows that it is feasible to select characteristic wavelengths for the detection of slight bruises on pears by the methods combining the PCA and ROC analysis. This study can lay a foundation for the development of an online detection system for slight bruise detection on pears.

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Detecting macronutrients content and distribution in oilseed rape leaves based on hyperspectral imaging

Cited by 121 publications

References 48 publications

Use of Fluorescence and Reflectance Spectra for Predicting Okra (<i>Abelmoschus esculentus</i>) Yield and Macronutrient Contents of Leaves

Use of Fluorescence and Reflectance Spectra for Predicting Okra (<i>Abelmoschus esculentus</i>) Yield and Macronutrient Contents of Leaves

Mapping spatial variability of crop growth conditions using RapidEye data in Northern Ontario, Canada

Wavelength Selection for Detection of Slight Bruises on Pears Based on Hyperspectral Imaging

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Detecting macronutrients content and distribution in oilseed rape leaves based on hyperspectral imaging

Cited by 121 publications

References 48 publications

Use of Fluorescence and Reflectance Spectra for Predicting Okra (&lt;i&gt;Abelmoschus esculentus&lt;/i&gt;) Yield and Macronutrient Contents of Leaves

Use of Fluorescence and Reflectance Spectra for Predicting Okra (&lt;i&gt;Abelmoschus esculentus&lt;/i&gt;) Yield and Macronutrient Contents of Leaves

Mapping spatial variability of crop growth conditions using RapidEye data in Northern Ontario, Canada

Wavelength Selection for Detection of Slight Bruises on Pears Based on Hyperspectral Imaging

Contact Info

Product

Resources

About

Use of Fluorescence and Reflectance Spectra for Predicting Okra (<i>Abelmoschus esculentus</i>) Yield and Macronutrient Contents of Leaves

Use of Fluorescence and Reflectance Spectra for Predicting Okra (<i>Abelmoschus esculentus</i>) Yield and Macronutrient Contents of Leaves