Identification of species of the genus Acer L. using vegetation indices calculated from the hyperspectral images of leaves

Dmitriev, Pavel; Kozlovsky, Boris L.; Kupriushkin, Denis P.; Лысенко, В. С.; Rajput, Vishnu D.; Ignatova, Maria A.; Tarik, Ekaterina P.; Kapralova, Olga A.; Tokhtar, Valeriy K.; Singh, Anurag; Minkina, Tatiana; Varduni, Tatiana V.; Sharma, Meenakshi; Taloor, Ajay Kumar; Thapliyal, Asha

doi:10.1016/j.rsase.2021.100679

Cited by 7 publications

(3 citation statements)

References 94 publications

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“…Finally, the spectral data of each sample as collected by the hyperspectral camera with an exposure time of 30 ms. During hyperspectral image acquisition, the leaves were placed flat on a black platform with the adaxial side facing the camera. 17,18 The shooting time of one hyperspectral image was about 20 s per sample. The effects of heat on leaf samples caused by the lights could be ignored in such a short time and no obvious changes were observed for leaves after hyperspectral image acquisition.…”

Section: Hyperspectral Imaging System and Image Acquisitionmentioning

confidence: 99%

Leaf-based species classification of hybrid cherry tomato plants by using hyperspectral imaging

Wu²,

Zhao

et al. 2023

Journal of Near Infrared Spectroscopy

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Approaches based on near infrared hyperspectral imaging (NIR-HSI) technology combined with machine learning have been developed to classify the leaves of hybrid cherry tomatoes and then identify the species of hybrid cherry tomato plants. The near infrared (NIR) hyperspectral images of 400 cherry tomato leaves (100 per species) were collected in the wavelength range of 900–1700 nm. Machine learning algorithms such as linear discriminant analysis (LDA), random forest (RF), and support vector machine (SVM) were employed to construct leaf classification models with the hyperspectral data preprocessed by Savitzky-Golay (SG) smoothing filter, first derivative (first Der) and standard normal variate (SNV). Principle of Component Analysis (PCA) was also used to reduce the data dimension and extract spectral features. It is revealed that the LDA model reaches the highest classification accuracy among the three machine learning algorithms and SNV can lead to higher improvement in model accuracy than other preprocessing methods of SG smoothing and first Der. Analysis based on PCA spectral feature extraction demonstrates that differences occur in internal material content in the leaves of cherry tomato plants with different species, which renders the models being able to distinguish between the species. Another important work was performed to reveal the different effects of the mesophyll and vein regions (VR) on the accuracy of the leaf classification model. It is demonstrated that the classification accuracy is improved by a value of 0.033 or 0.042 when mesophyll substitutes vein or whole leaf as regions of interest (ROI) to extract reflectance spectra for modeling. As a result, the accuracy of the training and test set respectively reached a high value of 0.998 and 0.973 for the LDA classification model combined with the SNV preprocessing method. The results propose that the use of mesophyll region (MR) as ROI can improve the performance of the leaf classification model, which provides a new strategy for efficient and non-destructive classification of different hybrid cherry tomato plants.

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Section: Hyperspectral Imaging System and Image Acquisitionmentioning

confidence: 99%

Leaf-based species classification of hybrid cherry tomato plants by using hyperspectral imaging

Wu²,

Zhao

et al. 2023

Journal of Near Infrared Spectroscopy

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“…However, there are also more subtle changes in spectral derivatives of leaf-rolled plants exposed to drought treatment, mostly caused by the accumulation/translocation of biochemicals and decrease in photosynthetic activity [32], allowing the drought detection using hyperspectral data along with several derived normalized vegetation indexes such as normalized difference vegetation index (NDVI) [33]. Normalized difference vegetation index and other normalized vegetation indices (VI) represent a useful and sensitive tool in vegetation monitoring converting the raw sensor reads to useful normalized and repeatable results [34,35]. Hyper/multispectral monitoring is already a proven method of vegetation monitoring [36,37] coming more and more to focus of researchers addressing high-throughput phenotyping and precision agriculture [38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in the Analysis of Multispectral Reads in Maize Canopies Responding to Increased Temperatures and Water Deficit

et al. 2022

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Real-time monitoring of crop responses to environmental deviations represents a new avenue for applications of remote and proximal sensing. Combining the high-throughput devices with novel machine learning (ML) approaches shows promise in the monitoring of agricultural production. The 3 × 2 multispectral arrays with responses at 610 and 680 nm (red), 730 and 760 nm (red-edge) and 810 and 860 nm (infrared) spectra were used to assess the occurrence of leaf rolling (LR) in 545 experimental maize plots measured four times for calibration dataset (n = 2180) and 145 plots measured once for external validation. Multispectral reads were used to calculate 15 simple normalized vegetation indices. Four ML algorithms were assessed: single and multilayer perceptron (SLP and MLP), convolutional neural network (CNN) and support vector machines (SVM) in three validation procedures, which were stratified cross-validation, random subset validation and validation with external dataset. Leaf rolling occurrence caused visible changes in spectral responses and calculated vegetation indexes. All algorithms showed good performance metrics in stratified cross-validation (accuracy >80%). SLP was the least efficient in predictions with external datasets, while MLP, CNN and SVM showed comparable performance. Combining ML with multispectral sensing shows promise in transition towards agriculture based on data-driven decisions especially considering the novel Internet of Things (IoT) avenues.

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“…Содержание фотосинтетических пигментов в листьях видоспецифично [36] и тесно связано с биологической продуктивностью. Их содержание и пропорции являются результатом эволюционного приспособления к условиям произрастания вида, при этом фотосинтетическая система чутко реагирует на изменения внешних условий, в первую очередь освещенность.…”

Section: Introductionunclassified

Сезонная Динамика Фотосинтетических Пигментов У Кленов Acer Campestre L., A. Negundo L. И A. Saccharinum L. В Ростове-На-Дону

et al. 2022

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The objective of the study carried out at Botanical Garden of Southern Federal University (Rostov-on-Don, Russia) was to assess the seasonal changes in photosynthetic pigments contents in the leaves of the maples Acer campestre L., A. negundo L., and A. saccharinum L. Leaves samples were collected from selected maple trees once a week. The contents of chlorophylls and carotenoids were determined spectrophotometrically and expressed per leaf area (mg/dm2). Similar seasonal changes in chlorophylls a and b in A. campestre, A. saccharinum, and A. negundo have been found. At the same time, the seasonal changes in carotenoids contents in A. campestre differs from those in A. saccharinum and A. negundo. The qualitative and quantitative parameters of the seasonal changes in chlorophylls a and b and carotenoids differ from each other, regardless of species. All maple species differ significantly in the mean contents of photosynthetic pigments, the highest one featured by A. campestre. Using the analysis of variance, variations in the contents of photosynthetic pigments during a season were assessed. Chlorophyll variability is highest in A. campestre and lowest in A. negundo. A very large difference of A. campestre from A. saccharinum and A. negundo was found in the variation of carotenoid contents. The response of photosynthetic pigments to changes in climatic factors is higher in A. campestre than in A. saccharinum and A. negundo. These findings suggest that drought adaptation strategy of A. campestre is active, whereas that of A. saccharinum and A. negundo is passive. Keywords: chlorophyll, carotenoids, maple, adaptation, climatic factors.

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Identification of species of the genus Acer L. using vegetation indices calculated from the hyperspectral images of leaves

Cited by 7 publications

References 94 publications

Leaf-based species classification of hybrid cherry tomato plants by using hyperspectral imaging

Leaf-based species classification of hybrid cherry tomato plants by using hyperspectral imaging

Machine Learning in the Analysis of Multispectral Reads in Maize Canopies Responding to Increased Temperatures and Water Deficit

Сезонная Динамика Фотосинтетических Пигментов У Кленов Acer Campestre L., A. Negundo L. И A. Saccharinum L. В Ростове-На-Дону

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