High-throughput phenotyping to detect anthocyanins, chlorophylls, and carotenoids in red lettuce germplasm

Clemente, Andressa Alves; Maciel, Gabriel Mascarenhas; Siquieroli, Ana Carolina Silva; Gallis, Rodrigo Bezerra de Araújo; Pereira, Lucas Medeiros; Duarte, Jéssyca Gonçalves

doi:10.1016/j.jag.2021.102533

Cited by 11 publications

(16 citation statements)

References 57 publications

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“…For field crops, unmanned aerial vehicle (UAV) multispectral and low-cost RGB (red, green, blue) imaging platforms are considered promising high throughput field phenotyping tools and have been widely verified in crops phenotyping [17][18][19]. Vegetation indices (spectral indices) or color indices derived from multispectral or RGB imagery are widely used in crop diversified phenotyping, such as pigments (chlorophyll, carotenoids, anthocyanins) content detection [20], phenology determination [21][22][23][24], canopy greenness, or vigor assessment [25,26], yield prediction [27][28][29], and stress monitoring [30,31].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of UAV RGB and Multispectral Imaging in Phenotyping for Stay Green of Wheat Population

Cao

Liu

et al. 2021

Remote Sensing

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High throughput phenotyping (HTP) for wheat (Triticum aestivum L.) stay green (SG) is expected in field breeding as SG is a beneficial phenotype for wheat high yield and environment adaptability. The RGB and multispectral imaging based on the unmanned aerial vehicle (UAV) are widely popular multi-purpose HTP platforms for crops in the field. The purpose of this study was to compare the potential of UAV RGB and multispectral images (MSI) in SG phenotyping of diversified wheat germplasm. The multi-temporal images of 450 samples (406 wheat genotypes) were obtained and the color indices (CIs) from RGB and MSI and spectral indices (SIs) from MSI were extracted, respectively. The four indices (CIs in RGB, CIs in MSI, SIs in MSI, and CIs + SIs in MSI) were used to detect four SG stages, respectively, by machine learning classifiers. Then, all indices’ dynamics were analyzed and the indices that varied monotonously and significantly were chosen to calculate wheat temporal stay green rates (SGR) to quantify the SG in diverse genotypes. The correlations between indices’ SGR and wheat yield were assessed and the dynamics of some indices’ SGR with different yield correlations were tracked in three visual observed SG grades samples. In SG stage detection, classifiers best average accuracy reached 93.20–98.60% and 93.80–98.80% in train and test set, respectively, and the SIs containing red edge or near-infrared band were more effective than the CIs calculated only by visible bands. Indices’ temporal SGR could quantify SG changes on a population level, but showed some differences in the correlation with yield and in tracking visual SG grades samples. In SIs, the SGR of Normalized Difference Red-edge Index (NDRE), Red-edge Chlorophyll Index (CIRE), and Normalized Difference Vegetation Index (NDVI) in MSI showed high correlations with yield and could track visual SG grades at an earlier stage of grain filling. In CIs, the SGR of Normalized Green Red Difference Index (NGRDI), the Green Leaf Index (GLI) in RGB and MSI showed low correlations with yield and could only track visual SG grades at late grain filling stage and that of Norm Red (NormR) in RGB images failed to track visual SG grades. This study preliminarily confirms the MSI is more available and reliable than RGB in phenotyping for wheat SG. The index-based SGR in this study could act as HTP reference solutions for SG in diversified wheat genotypes.

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Section: Introductionmentioning

confidence: 99%

A Comparison of UAV RGB and Multispectral Imaging in Phenotyping for Stay Green of Wheat Population

Cao

Liu

et al. 2021

Remote Sensing

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“…60 GNDVI is moderately correlated with chlorophyll. 61 GLI can reflect the color change information of vegetation from growth to senescence, and SAVI can reflect the growth status and cover information about plants. 62 Finally, vegetation index features are extracted from GF-1 and Sentinel-2 data, respectively.…”

Section: Datasetsmentioning

confidence: 99%

“…EVI gains higher sensitivity in high biomass regions and responds differently to light layers of different species 60 . GNDVI is moderately correlated with chlorophyll 61 . GLI can reflect the color change information of vegetation from growth to senescence, and SAVI can reflect the growth status and cover information about plants 62 .…”

Section: Data Acquisition and Experimental Settingsmentioning

confidence: 99%

Optimal feature extraction from multidimensional remote sensing data for orchard identification based on deep learning methods

Luo,

Guo,

Zhu

et al. 2024

J. Appl. Rem. Sens.

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Accurate orchard spatial distribution information can help government departments to formulate scientific and reasonable agricultural economic policies. However, it is prominent to apply remote sensing images to obtain orchard planting structure information. The traditional multidimensional remote sensing data processing, dimension reduction and classification, which are two separate steps, cannot guarantee that final classification results can be benefited from dimension reduction process. Consequently, to make connection between dimension reduction and classification, this work proposes two neural networks that fuse stack autoencoder and convolutional neural network (CNN) at one-dimension and three-dimension, namely one-dimension and three-dimension fusion stacked autoencoder (FSA) and CNN networks (1D-FSA-CNN and 3D-FSA-CNN). In both networks, the front-end uses a stacked autoencoder (SAE) for dimension reduction, and the back-end uses a CNN with a Softmax classifier for classification. In the experiments, based on Google Earth Engine platform, two groups of orchard datasets are constructed using multi-source remote sensing data (i.e., GaoFen-1, Sentinel-2 and GaoFen-1, and GaoFen-3). Meanwhile, DenseNet201, 3D-CNN, 1D-CNN, and SAE are used for conduct two comparative experiments. The experimental results show that the proposed fusion neural networks achieve the state-of-the-art performance, both accuracies of 3D-FSA-CNN and 1D-FSA-CNN are higher than 95%.

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“…Unmanned aerial vehicle (UAV) platforms were used for the measurement of various traits in horticultural crops. For example, UAV-based remote sensing coupled with different machine learning approaches was used for disease detection and classification in potato, tomato, banana, pear, and apple [ 16 , 17 , 18 , 19 , 20 , 21 , 22 ], for tree detection in orchards such as banana and citrus [ 23 , 24 , 25 ], for aboveground biomass estimation in onion, potato, tomato, and strawberry [ 26 , 27 , 28 , 29 ], and other traits of fruits and vegetables [ 23 , 30 , 31 ].…”

Section: High-throughput Phenotyping Platformsmentioning

confidence: 99%

Image-Based High-Throughput Phenotyping in Horticultural Crops

Abebe

Kim

et al. 2023

Plants

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Plant phenotyping is the primary task of any plant breeding program, and accurate measurement of plant traits is essential to select genotypes with better quality, high yield, and climate resilience. The majority of currently used phenotyping techniques are destructive and time-consuming. Recently, the development of various sensors and imaging platforms for rapid and efficient quantitative measurement of plant traits has become the mainstream approach in plant phenotyping studies. Here, we reviewed the trends of image-based high-throughput phenotyping methods applied to horticultural crops. High-throughput phenotyping is carried out using various types of imaging platforms developed for indoor or field conditions. We highlighted the applications of different imaging platforms in the horticulture sector with their advantages and limitations. Furthermore, the principles and applications of commonly used imaging techniques, visible light (RGB) imaging, thermal imaging, chlorophyll fluorescence, hyperspectral imaging, and tomographic imaging for high-throughput plant phenotyping, are discussed. High-throughput phenotyping has been widely used for phenotyping various horticultural traits, which can be morphological, physiological, biochemical, yield, biotic, and abiotic stress responses. Moreover, the ability of high-throughput phenotyping with the help of various optical sensors will lead to the discovery of new phenotypic traits which need to be explored in the future. We summarized the applications of image analysis for the quantitative evaluation of various traits with several examples of horticultural crops in the literature. Finally, we summarized the current trend of high-throughput phenotyping in horticultural crops and highlighted future perspectives.

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High-throughput phenotyping to detect anthocyanins, chlorophylls, and carotenoids in red lettuce germplasm

Cited by 11 publications

References 57 publications

A Comparison of UAV RGB and Multispectral Imaging in Phenotyping for Stay Green of Wheat Population

A Comparison of UAV RGB and Multispectral Imaging in Phenotyping for Stay Green of Wheat Population

Optimal feature extraction from multidimensional remote sensing data for orchard identification based on deep learning methods

Image-Based High-Throughput Phenotyping in Horticultural Crops

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