Close range hyperspectral imaging of plants: A review

Mishra, Puneet; Asaari, Mohd Shahrimie Mohd; Herrero-Langreo, Ana; Lohumi, Santosh; Diezma, Belén; Scheunders, Paul

doi:10.1016/j.biosystemseng.2017.09.009

Cited by 238 publications

(143 citation statements)

References 92 publications

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“…The spectra at the range of 1000-2500 nm are near-infrared region which relates to the chemical compositions. The sub-ranges of 400-2500 nm have been widely studied in plant science [72]. SVC has also been used in various studies based on different spectral ranges.…”

Section: Summary Of Pixel-wise Predicted Mean Values Of All Samples Fmentioning

confidence: 99%

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Evaluation of quinclorac toxicity and alleviation by salicylic acid in rice seedlings using ground-based visible/near-infrared hyperspectral imaging

et al. 2020

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Background: To investigate potential effects of herbicide phytotoxic on crops, a major challenge is a lack of nondestructive and rapid methods to detect plant growth that could allow characterization of herbicide-resistant plants.In such a case, hyperspectral imaging can quickly obtain the spectrum for each pixel in the image and monitor status of plants harmlessly. Method:Hyperspectral imaging covering the spectral range of 380-1030 nm was investigated to determine the herbicide toxicity in rice cultivars. Two rice cultivars, Xiushui 134 and Zhejing 88, were respectively treated with quinclorac alone and plus salicylic acid (SA) pre-treatment. After ten days of treatments, we collected hyperspectral images and physiological parameters to analyze the differences. The score images obtained were used to explore the differences among samples under diverse treatments by conducting principal component analysis on hyperspectral images. To get useful information from original data, feature extraction was also conducted by principal component analysis. In order to classify samples under diverse treatments, full-spectra-based support vector classification (SVC) models and extracted-feature-based SVC models were established. The prediction maps of samples under different treatments were constructed by applying the SVC models using extracted features on hyperspectral images, which provided direct visual information of rice growth status under herbicide stress. The physiological analysis with the changes of stress-responsive enzymes confirmed the differences of samples under different treatments. Results:The physiological analysis showed that SA alleviated the quinclorac toxicity by stimulating enzymatic activity and reducing the levels of reactive oxygen species. The score images indicated there were spectral differences among the samples under different treatments. Full-spectra-based SVC models and extracted-feature-based SVC models obtained good results for the aboveground parts, with classification accuracy over 80% in training, validation and prediction set. The SVC models for Zhejing 88 presented better results than those for Xiushui 134, revealing the different herbicide tolerance between rice cultivars. © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article' s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article' Conclusion:We develop a reliable and effective model using hyperspectral imaging technique which enables the evaluation and visualization of herbicide toxicity for rice. The reflectance spectra variations of rice could reveal the stress sta...

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Section: Summary Of Pixel-wise Predicted Mean Values Of All Samples Fmentioning

confidence: 99%

“…The original raw images were corrected to reflectance hyperspectral images for further processing. The calibrated image Ic was calculated using the following equation [72]:…”

Section: Hyperspectral Imaging System and Image Acquisitionmentioning

confidence: 99%

Evaluation of quinclorac toxicity and alleviation by salicylic acid in rice seedlings using ground-based visible/near-infrared hyperspectral imaging

et al. 2020

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“…The imaging modality provides access to the structure of the plants whereas the spectroscopy provides insight into the chemical state of the plants. Visible and near-infrared spectroscopy (VNIRS (400-1000nm)) HSI is of particular interest as it provides access to the key chemical pigments, which can be directly relatable to the photosynthetic activity of plants as well as the moisture and internal structure of plant leaves [2].…”

Section: Introductionmentioning

confidence: 99%

Early Detection Of Drought Stress in Arabidopsis Thaliana Utilsing a Portable Hyperspectral Imaging Setup

Mishra

Feller

Schmuck

et al. 2019

2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)

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Close-range hyperspectral imaging (HSI) of plants is now a potential tool for non-destructive extraction of plant functional traits. A major motivation is the plant phenotyping related applications where different plant genotypes are explored for different environmental conditions. HSI of Arabidopsis thaliana is of particular importance as it is a model organism in plant biology. In the present work, a portable HSI setup has been used for the monitoring of a set of 6 Arabidopsis thaliana plants. The plants were monitored under controlled watering conditions where 3 plants were watered as normal and the other 3 plants were given 50% of the normal volume of water. The images were pre-processed utilising the standard normal variate (SNV) and changes over time were evaluated using unsupervised clustering over the time series. The results showed an early detection of stress from day 4 onwards compared to the commonly used normalised difference vegetation index (NDVI), which provided detection from day 9.

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“…A major benefit of HSI compared to other imaging sensors is that it provides complementary spectroscopic and imaging information [2]. The visible region provides access to the pigments' concentration and the near-infrared region provides information related to the moisture and internal structure of leaves [3]. However, implementing HSI for automated characterisation of plants in a HTPPS is a challenging task; different issues arise starting from handling and imaging plants to the processing of the huge volume of data generated by phenotyping experiments.…”

Section: Introductionmentioning

confidence: 99%

Homogenising and Segmenting Hyperspectral Images of Plants and Testing Chemicals in a High-Throughput Plant Phenotyping Setup

Mishra

Schmuck

Roth

et al. 2019

2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)

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Use of hyperspectral imaging (HSI) for automated characterisation of plants in a high-throughput plant phenotyping setup (HTPPS) is a challenging task. A challenge arises when the same plant is being monitored automatically during the experiment as it might not be in the same orientation as it was imaged last time. Such changes in orientation result in variations in illumination, which affects the signals recorded by the HSI setup. In addition, there are challenges with the use of threshold-based segmentation approaches such as normalised difference vegetation index (NDVI) for distinguishing between old and dead leaves, which might be observed in the later stages of experiments, from the soil background. Therefore, the potential of spectral normalisation for homogenising HS images and the use of supervised spectral set for plant segmentation is presented. Further, the effects of testing chemicals on plants were visualised using PCA of the HS images.

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Close range hyperspectral imaging of plants: A review

Cited by 238 publications

References 92 publications

Evaluation of quinclorac toxicity and alleviation by salicylic acid in rice seedlings using ground-based visible/near-infrared hyperspectral imaging

Evaluation of quinclorac toxicity and alleviation by salicylic acid in rice seedlings using ground-based visible/near-infrared hyperspectral imaging

Early Detection Of Drought Stress in Arabidopsis Thaliana Utilsing a Portable Hyperspectral Imaging Setup

Homogenising and Segmenting Hyperspectral Images of Plants and Testing Chemicals in a High-Throughput Plant Phenotyping Setup

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