Nondestructive Detection of Nitrogen in Chinese Cabbage Leaves Using VIS–NIR Spectroscopy

Min, Min; Lee, Won Suk; Kim, Yong Hyeon; Bucklin, R. A.

doi:10.21273/hortsci.41.1.162

Cited by 49 publications

(26 citation statements)

References 14 publications

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“…Figure 2b shows the average spectral reflectance curves of different dates. The wavelength at 555 nm is the nitrogen absorption band which correlates to absorbed light by nitrogen within the crop tissue [27] . It shows that spectral reflectance values at 555 nm generally increased with time.…”

Section: Spectral Featuresmentioning

confidence: 99%

Spectral reflectance response to nitrogen fertilization in field grown corn

Xie¹,

Yang²,

Hummel³

et al. 2018

International Journal of Agricultural and Biological Engineering

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This study was carried out to analyze the spectral reflectance response of different nitrogen levels for corn crops. Four different nitrogen treatments of 0%, 80%, 100% and 120% BMP (best management practice) were studied. Principal component analysis-loading (PCA-loading) was used to identify the effective wavelengths. Partial least squares (PLS) and multiple linear regression (MLR) models were built to predict different nitrogen values. Vegetation indices (VIs) were calculated and then used to build more prediction models. Both full and selected wavelengths-based models showed similar prediction trends. The overall PLS model obtained the coefficient of determination (R 2 ) of 0.6535 with a root mean square error (RMSE) of 0.2681 in the prediction set. The selected wavelengths for overall MLR model obtained the R 2 of 0.6735 and RMSE of 0.3457 in the prediction set. The results showed that the wavelengths in visible and near infrared region (350-1000 nm) performed better than the two either spectral regions (1001-1350/1425-1800 nm and 2000-2400 nm). For each data set, the wavelengths around 555 nm and 730 nm were identified to be the most important to predict nitrogen rates. The vogelmann red edge index 2 (VOG 2) performed the best among all VIs. It demonstrated that spectral reflectance has the potential to be used for analyzing nitrogen response in corn.

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Section: Spectral Featuresmentioning

confidence: 99%

Spectral reflectance response to nitrogen fertilization in field grown corn

Xie¹,

Yang²,

Hummel³

et al. 2018

International Journal of Agricultural and Biological Engineering

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“…It probably influences the accuracy of model for the prediction of FLAV and NBI. Correlational study was found by Min et al (2006), who stressed that the regions of 1910 and 1938 nm highly related to water might have a strong impact on the N concentration prediction.…”

Section: Discussionmentioning

confidence: 99%

Association of Spectroscopically Determined Leaf Nutrition Related Traits and Breeding Selection in Sassafras Tzumu

Liu

Tan

et al. 2020

Preprint

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Background: The nutrition related to traits is an influential role in tree growth, tree production and nutrient cycling. Therefore, the influence of genetic parameters on leaf nutrition traits ought to take account of optimal tree breeding selection. However, the measurement methods are seriously affected by the progress of breeding selection program. In this study, we tested the ability of spectroscopy to quantify the specific leaf nutrition traits including Anthocyanins (ANTH), flavonoids (FLAV) and Nitrogen balance index (NBI), and estimated the genetic variation of these leaf traits based on the spectroscopic predicted data. Live fresh leaves of Sassafras tzumu were selected for spectral collection, after which concentrations of ANTH, FLAV and NBI were analyzed by standard analytical methods. Partial least squares regression (PLSR), five spectra pre-processing methods, and four variable selection algorisms were conducted for the optimal prediction model selection. Each trait model was simulated 200 times for error estimation.Results: The stander normal variation (SNV) to the ANTH model and 1st derivatives to the FLAV and NBI models, combined with significant Multivariate Correlation (sMC) algorithm variable selection are finally regarded as the best performance model. The ANTH model produced the highest accuracy of prediction with a mean R2 of 0.72 and mean RMSE of 0.10 %, followed by FLAV and NBI model (mean R2 =0.58, mean RMSE = 0.11 % and mean R2 =0.44, mean RMSE = 0.04 %). High heritability was found of ANTH FLAV and NBI with h2 of 0.78, 0.58 and 0.61 respectively. It shows that it is benefitting and possible of breeding selection for the improvement of leaf nutrition traits.Conclusions: Spectroscopy can successfully characterize the leaf nutrition traits in living tree leaves and the ability to simultaneous multiple plant traits provides a promising and high-throughput tool for the quick analysis of large size samples and serves for genetic breeding program.

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“…Zhang et al developed a handhold spectral instrument to diagnose the growth status of the crop in greenhouse using optical fibers [5] . Min et al investigated the use of a visible and NIR spectroscopy to estimate nitrogen concentration of Chinese cabbage [12] . Xu, Z. et al developed an optical sensor after analyzing the optical characters of spectral reflectance of canopy and the optical principle of non-destructive nitrogen monitoring [13] .…”

Section: Research Backgroundmentioning

confidence: 99%

Development of a wireless crop growth monitor based on optical principle

Cui

2008

SPIE Proceedings

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In order to detect the plants' nitrogen content in real-time, a wireless crop growth monitor is developed. It is made up of a sensor and a controller. The sensor consists of an optical part and a circuit part. The optical part is made up of 4 optical channels and 4 photo-detectors. 2 channels receive the sunlight and the other 2 receive the reflected light from the crop canopy. The intensity of sunlight and the reflected light is measured at the wavebands of 610 nm and 1220 nm respectively. The circuit part is made up of power supply unit, 4 amplifiers and a wireless module. The controller has functions such as keyboard input, LCD display, data storage, data upload and so on. Both hardware and software are introduced in this report. Calibration tests show that the optical part has a high accuracy and the wireless transmission also has a good performance.

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Nondestructive Detection of Nitrogen in Chinese Cabbage Leaves Using VIS–NIR Spectroscopy

Cited by 49 publications

References 14 publications

Spectral reflectance response to nitrogen fertilization in field grown corn

Spectral reflectance response to nitrogen fertilization in field grown corn

Association of Spectroscopically Determined Leaf Nutrition Related Traits and Breeding Selection in Sassafras Tzumu

Development of a wireless crop growth monitor based on optical principle

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