Nondestructive Estimation of Nitrogen Status and Vegetation Index of Oilseed Rape Canopy Using Multi-Spectral Imaging Technology

Abstract Background Pyropia is an economically advantageous genus of red macroalgae, which has been cultivated in the coastal areas of East Asia for over 300 years. Realizing estimation of macroalgae biomass in a high-throughput way would great benefit their cultivation management and research on breeding and phenomics. However, the conventional method is labour-intensive, time-consuming, manually destructive, and prone to human error. Nowadays, high-throughput phenotyping using unmanned aerial vehicle (UAV)-based spectral imaging is widely used for terrestrial crops, grassland, and forest, but no such application in marine aquaculture has been reported. Results In this study, multispectral images of cultivated Pyropia yezoensis were taken using a UAV system in the north of Haizhou Bay in the midwestern coast of Yellow Sea. The exposure period of P. yezoensis was utilized to prevent the significant shielding effect of seawater on the reflectance spectrum. The vegetation indices of normalized difference vegetation index (NDVI), ratio vegetation index (RVI), difference vegetation index (DVI) and normalized difference of red edge (NDRE) were derived and indicated no significant difference between the time that P. yezoensis was completely exposed to the air and 1 h later. The regression models of the vegetation indices and P. yezoensis biomass per unit area were established and validated. The quadratic model of DVI (Biomass = − 5.550DVI2 + 105.410DVI + 7.530) showed more accuracy than the other index or indices combination, with the highest coefficient of determination (R2), root mean square error (RMSE), and relative estimated accuracy (Ac) values of 0.925, 8.06, and 74.93%, respectively. The regression model was further validated by consistently predicting the biomass with a high R2 value of 0.918, RMSE of 8.80, and Ac of 82.25%. Conclusions This study suggests that the biomass of Pyropia can be effectively estimated using UAV-based spectral imaging with high accuracy and consistency. It also implied that multispectral aerial imaging is potential to assist digital management and phenomics research on cultivated macroalgae in a high-throughput way.

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Nondestructive estimation of bok choy nitrogen status with an active canopy sensor in comparison to a chlorophyll meter

Pedosphere ◽

10.1016/s1002-0160(20)60037-6 ◽

2020 ◽

Vol 30 (6) ◽

pp. 769-777 ◽

Cited By ~ 1

Author(s):

Rongting JI ◽

Weiming SHI ◽

Yuan WANG ◽

Hailin ZHANG ◽

Ju MIN

Keyword(s):

Nitrogen Status ◽

Chlorophyll Meter ◽

Nondestructive Estimation

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Optimization of matching coded aperture with detector based on compressed sensing spectral imaging technology

Chinese Optics ◽

10.3788/co.20201302.0290 ◽

2020 ◽

Vol 13 (2) ◽

pp. 290-301

Author(s):

刘铭鑫 LIU Ming-xin ◽

张新 ZHANG Xin ◽

王灵杰 WANG Ling-jie ◽

史广维 SHI Guang-wei ◽

吴洪波 WU Hong-bo ◽

...

Keyword(s):

Compressed Sensing ◽

Spectral Imaging ◽

Coded Aperture ◽

Imaging Technology

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Research on classification and recognition system based on miniaturized portable spectral imaging technology

Infrared and Laser Engineering ◽

10.3788/irla201948.1023001 ◽

2019 ◽

Vol 48 (10) ◽

pp. 1023001

Author(s):

张晨 Zhang Chen ◽

刘舒扬 Liu Shuyang ◽

赵安娜 Zhao Anna ◽

王天鹤 Wang Tianhe ◽

贾晓东 Jia Xiaodong

Keyword(s):

Spectral Imaging ◽

Recognition System ◽

Imaging Technology

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Staring acousto-optic spectral imaging technology with area FPA and discussion on its application of unmanned aerial vehicle (UAV) platforms

Infrared and Laser Engineering ◽

10.3788/m0001820164502.203001 ◽

2016 ◽

Vol 45 (2) ◽

pp. 203001

Author(s):

何志平 He Zhiping ◽

秦侠格 Qin Xiage ◽

徐映宇 Xu Yingyu

Keyword(s):

Unmanned Aerial Vehicle ◽

Spectral Imaging ◽

Imaging Technology ◽

Aerial Vehicle

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Drought Stress Detection in Juvenile Oilseed Rape Using Hyperspectral Imaging with a Focus on Spectra Variability

Remote Sensing ◽

10.3390/rs12203462 ◽

2020 ◽

Vol 12 (20) ◽

pp. 3462

Author(s):

Wiktor R. Żelazny ◽

Jan Lukáš

Keyword(s):

Drought Stress ◽

Hyperspectral Imaging ◽

Oilseed Rape ◽

Treatment Effects ◽

Vegetation Index ◽

Principal Component ◽

Hyperspectral Data ◽

Stress Detection ◽

Mean Values ◽

Standard Deviations

Hyperspectral imaging (HSI) has been gaining recognition as a promising proximal and remote sensing technique for crop drought stress detection. A modelling approach accounting for the treatment effects on the stress indicators’ standard deviations was applied to proximal images of oilseed rape—a crop subjected to various HSI studies, with the exception of drought. The aim of the present study was to determine the spectral responses of two cultivars, ‘Cadeli’ and ‘Viking’, representing distinctive water management strategies, to three types of watering regimes. Hyperspectral data cubes were acquired at the leaf level using a 2D frame camera. The influence of the experimental factors on the extent of leaf discolorations, vegetation index values, and principal component scores was investigated using Bayesian linear models. Clear treatment effects were obtained primarily for the vegetation indexes with respect to the watering regimes. The mean values of RGI, MTCI, RNDVI, and GI responded to the difference between the well-watered and water-deprived plants. The RGI index excelled among them in terms of effect strengths, which amounted to −0.96[−2.21,0.21] and −0.71[−1.97,0.49] units for each cultivar. A consistent increase in the multiple index standard deviations, especially RGI, PSRI, TCARI, and TCARI/OSAVI, was associated with worsening of the hydric regime. These increases were captured not only for the dry treatment but also for the plants subjected to regeneration after a drought episode, particularly by PSRI (a multiplicative effect of 0.33[0.16,0.68] for ‘Cadeli’). This result suggests a higher sensitivity of the vegetation index variability measures relative to the means in the context of the oilseed rape drought stress diagnosis and justifies the application of HSI to capture these effects. RGI is an index deserving additional scrutiny in future studies, as both its mean and standard deviation were affected by the watering regimes.

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Leukemic cells segmentation algorithm based on molecular spectral imaging technology

10.1117/12.830539 ◽

2009 ◽

Cited By ~ 4

Author(s):

Qingli Li ◽

Chunni Dai ◽

Hongjing Liu ◽

Jingao Liu

Keyword(s):

Spectral Imaging ◽

Segmentation Algorithm ◽

Leukemic Cells ◽

Imaging Technology

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Research on snapshot infrared computational spectral imaging technology

AOPC 2020: Infrared Device and Infrared Technology ◽

10.1117/12.2580389 ◽

2020 ◽

Author(s):

Yanli Liu ◽

Haibo Zhao ◽

Dunliang Shen ◽

Jing Xu ◽

Fang Xue ◽

...

Keyword(s):

Spectral Imaging ◽

Imaging Technology

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Restoration Method of Hadamard Coding Spectral Imager

Applied Spectroscopy ◽

10.1177/0003702819900381 ◽

2020 ◽

Vol 74 (5) ◽

pp. 583-596

Author(s):

Xingjia Tang ◽

Zongben Xu ◽

Libo Li ◽

Shuang Wang ◽

Bingliang Hu ◽

...

Keyword(s):

Simulation Analysis ◽

Hadamard Matrix ◽

Engineering Application ◽

Spectral Imaging ◽

Inverse Solution ◽

Inverse Transformation ◽

Reconstruction Method ◽

Imaging Technology ◽

Restoration Method ◽

Spectral Imager

Hadamard coding spectral imaging technology is a computational spectral imaging technology, which modulates the target’s spectral information and recovers the original spectrum by inverse transformation. Because it has the advantage of multichannel detection, it is being studied by more researchers. For the engineering realization of push-broom coding spectral imaging instrument, it will inevitably be subjected to push-broom error, template error and detection noise, the redundant sampling problem caused by detector. Therefore, three restoration methods are presented in this paper: firstly, the one is the least squares solution, the two is the zero-filling inverse solution by extending the coding matrix in the redundant coding state to a complete higher order Hadamard matrix, the three is sparse method. Secondly, the numerical and principle analysis shows that the inverse solution of zero-compensation has better robustness and is more suitable for engineering application; its conditional number, error expectation and covariance are better and more stable because it directly uses Hadamard matrix, which has good generalized orthogonality. Then, a real-time spectral reconstruction method is presented, which is based on inverse solution of zero-compensation. Finally, simulation analysis shows that spectral data could be destructed relative accuracy in the error condition; however, the effect of template noise and push error on reconstruction is much greater than that of detection error. Therefore, in addition to reducing the detection noise as much as possible, lower template noise and more accurate push controlling should be guaranteed specifically in engineering realization.

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