Inter‐ and intraspecific diversity in <i>Cistus</i> L. (Cistaceae) seeds, analysed with computer vision techniques

Bianco, Marisol Lo; Grillo, Oscar; Cañadas, Eva M.; Venora, Gianfranco; Bacchetta, Gianluigi

doi:10.1111/plb.12529

Cited by 19 publications

(11 citation statements)

References 44 publications

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“…For example, in a previous study [36, 37], the authors extracted features such as shape, color, and texture (contrast, correlation, homogeny, entropy) from wheat grains to classify their accessions. Moreover, in another study [38, 39], the authors used an elliptic Fourier descriptor and the texture feature set called Haralick's texture descriptors to characterize seeds of plants for taxonomic classification. With a representative feature extraction tool, Scale Invariant Features Transforms (SIFT), which acts as an invariant feature descriptor not only to scale but also rotation, illumination, and viewpoint, Wilf et al [40] generated codebooks for dictionary learning, and their results demonstrated the effectiveness of their approach on taxonomic classification through leaves.…”

Section: Reviewmentioning

confidence: 99%

“…They selected seven discriminative grain features, incorporating aspects of shape, color, and texture, and achieved greater than 99% accuracy on the grain classification task. Another group examined two taxonomic classification tasks: the Malva alliance taxa and genus Cistus taxa [38, 39]. They acquired digital images of seeds using a flatbed scanner; extracted morphometric, colorimetric, and textural seed features; and then performed taxonomic classification with stepwise linear discriminant analysis (LDA).…”

Section: Reviewmentioning

confidence: 99%

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Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective

et al. 2018

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Employing computer vision to extract useful information from images and videos is becoming a key technique for identifying phenotypic changes in plants. Here, we review the emerging aspects of computer vision for automated plant phenotyping. Recent advances in image analysis empowered by machine learning-based techniques, including convolutional neural network-based modeling, have expanded their application to assist high-throughput plant phenotyping. Combinatorial use of multiple sensors to acquire various spectra has allowed us to noninvasively obtain a series of datasets, including those related to the development and physiological responses of plants throughout their life. Automated phenotyping platforms accelerate the elucidation of gene functions associated with traits in model plants under controlled conditions. Remote sensing techniques with image collection platforms, such as unmanned vehicles and tractors, are also emerging for large-scale field phenotyping for crop breeding and precision agriculture. Computer vision-based phenotyping will play significant roles in both the nowcasting and forecasting of plant traits through modeling of genotype/phenotype relationships.

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Section: Reviewmentioning

confidence: 99%

Section: Reviewmentioning

confidence: 99%

Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective

et al. 2018

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“…In particular, it has been successfully applied for Astragalus maritimus and A. verrucosus [ 20 ], Astragalus sect. Melanocercis [ 21 ] Lavatera triloba aggregate [ 22 ], Astragalus tragacantha complex [ 23 ] and Lavatera L., Malva L., and Cistus L. [ 24 , 25 ]. Linear discriminant analysis (LDA) models have proven to be effective for the discrimination and quality classification of seeds when combined with imaging techniques [ 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

Morpho-Colorimetric Characterization of the Sardinian Endemic Taxa of the Genus Anchusa L. by Seed Image Analysis

Farris

Orru²,

Ucchesu

et al. 2020

Plants

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In this work, the seed morpho-colorimetric differentiation of the Sardinian endemic species of Anchusa (Boraginaceae) was evaluated. In Sardinia, the Anchusa genus includes the following seven taxa: A. capellii, A. crispa ssp. crispa, A. crispa ssp. maritima, A. formosa, A. littorea, A. montelinasana, and A. sardoa. Seed images were acquired using a flatbed scanner and analyzed using the free software package ImageJ. A total of 74 seed morpho-colorimetric features of 2692 seed lots of seven taxa of Anchusa belonging to 17 populations were extrapolated and used to build a database of seed size, shape, and color features. The data were statistically elaborated by the stepwise linear discriminant analysis (LDA) to compare and discriminate each accession and taxon. In addition, the seed morpho-colorimetric differences among coastal and mountainous taxa were evaluated. Considering the ecological conditions, the LDA was able to discriminate among the Anchusa taxa with a correct identification of 87.4% and 90.8% of specimens for mountainous and coastal plants, respectively. Moreover, the LDA of the 17 populations of Anchusa showed a low separation among species and populations within the coastal group, highlighting how the long-distance dispersal by flotation on the sea water surface and the pollination network may influence the similarity patterns observed. In addition, a misattribution was observed for A. crispa ssp. crispa, which was misclassified as A. crispa ssp. maritima in 14.1% of cases, while A. crispa ssp. maritima was misidentified as A. crispa ssp. crispa in 21.1% of cases, highlighting a close phenotypic relationship between these two taxa. The statistical results obtained through the seed image analysis showed that the morpho-colorimetric features of the seeds provide important information about the adaptation and evolution of Anchusa taxa in Sardinia.

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“…However, despite the low number of species (31 species are listed in Euro+Med PlantBase [7]), the genus Cistus is taxonomically complex [8]. Difficulties, uncertainties, and ambiguities in taxonomic classification and identification of Cistus species and subspecies arise from strong hybridisation between species, ecotypic differentiation (e.g., Cistus albidus, Cistus creticus) [2,9], morphological polymorphism of some species (e.g., C. creticus, Cistus monspeliensis) [8,9], and unclear generic boundaries and intra-generic organisation, as well as inconsistent naming of taxa as, e.g., the putative synonymous use of C. creticus, Cistus villosus, and Cistus incanus var. creticus in literature [2,10,11].…”

Section: Introductionmentioning

confidence: 99%

Differentiation between Cistus L. (Sub-) Species (Cistaceae) Using NMR Metabolic Fingerprinting

et al. 2020

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The genus Cistus is taxonomically complex, as taxonomic classification of individual species based on morphological criteria is often difficult and ambiguous. However, specific species contain valuable natural products, especially terpenoids and polyphenols, which exert various biological effects and might therefore be used for treatment of a broad array of disorders. Hence, a fast and reliable method for clear identification of different Cistus (sub-) species is required. Approaches for analysis of secondary metabolite profiles, e.g., with NMR, might remedy the challenging classification of Cistus (sub-) species and help to identify specific markers for differentiation between them. In the present study, 678 samples from wild-growing Cistus populations, including 7 species and 6 subspecies/varieties thereof, were collected in 3 years from populations in 11 countries all over the Mediterranean basin. Samples were extracted with buffered aqueous methanol and analysed with NMR. From the resulting 1D-1H-NOESY and J-Res profile spectra, marker signals or spectral regions for the individual (sub-) species were identified with multivariate statistical tools. By examining the NMR profiles of these extracts, we were able to identify discriminators and specific markers for the investigated Cistus (sub-) species. Various influencing factors, like (sub-) species, wild harvestings of different populations from several countries, numerous collection sites, different years, and cultivation in greenhouses have been considered in this work. As the here identified markers are independent from these influencing factors, the results can be considered a robust model and might be used for future differentiation between Cistus (sub-) species.

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Inter‐ and intraspecific diversity in Cistus L. (Cistaceae) seeds, analysed with computer vision techniques

Cited by 19 publications

References 44 publications

Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective

Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective

Morpho-Colorimetric Characterization of the Sardinian Endemic Taxa of the Genus Anchusa L. by Seed Image Analysis

Differentiation between Cistus L. (Sub-) Species (Cistaceae) Using NMR Metabolic Fingerprinting

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