Phenotypic identification of plum varieties ( Prunus domestica L.) by endocarps morpho-colorimetric and textural descriptors

Sarigu, Marco; Grillo, Oscar; Bianco, Marisol Lo; Ucchesu, Mariano; Droogmans, Guy; Loi, Maria Cecilia; Venora, Gianfranco; Bacchetta, Gianluigi

doi:10.1016/j.compag.2017.02.009

Cited by 28 publications

(17 citation statements)

References 35 publications

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“…This effort is essential to prospect strategies for the implementation of adequate on-farm conservation schemes and it is a prerequisite for the utilization of plant genetic resources for sustainable agriculture [16]. The evaluation of morphological traits is an established and important method for the description and identification of plum varieties [13,17,18]. In addition, DNA analysis provides information that is independent of environmental effects.…”

Section: Introductionmentioning

confidence: 99%

Molecular and Phenotypic Diversity of Traditional European Plum (Prunus domestica L.) Germplasm of Southern Italy

et al. 2019

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The European plum (Prunus domestica L.) is a worldwide distributed tree species. Italy has an ample number of traditional varieties, but many are neglected and at risk of extinction. This germplasm is still cultivated in rural areas in spite of the EU-28 crisis of the stone fruit sector. Traditional European plum varieties remain poorly characterized, strongly limiting their promotion in local markets, use for farm diversification, and exploitation for local gastronomic products. In this study, we carried out an investigation of the morphological and genetic diversity present in an ex-situ collection of 29 traditional varieties of P. domestica of the Campania region (Southern Italy). The combination of five continuous and five categorical fruit traits allowed us to phenotypically distinguish all the varieties. Similarly, the Simple Sequence Repeats (SSRs) employed (five genomic and five deriving from Expressed Sequence Tags) identified a unique molecular profile for each variety. Moreover, the data indicated that the phenotypic and molecular investigations provided different clustering, suggesting that the two analyses sampled different sources of diversity. The number and the distribution of the scored phenotypes as well as the indices of genetic diversity imply the presence of a wide-ranging variation, which may sustain the development of high-value, niche market products. Our work provided evidence that the implementation of measures for a combination of ex-situ and on-farm conservation of traditional European plum varieties should be preferred to avoid the loss of an ample diversity.

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Section: Introductionmentioning

confidence: 99%

Molecular and Phenotypic Diversity of Traditional European Plum (Prunus domestica L.) Germplasm of Southern Italy

et al. 2019

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“…The works most related to ours belong to this category. In particular, Sarigu et al [5] performed a plum variety identification employing seed's endocarp shape, colour, and texture descriptors followed by a Linear Discriminant Analysis (LDA) to obtain the most representative features, while Przybylo et al [21] and Loddo et al [6] employed CNNs.…”

Section: Seed Detection and Classificationmentioning

confidence: 99%

“…In general, computer vision techniques have been applied to this or similar tasks, even though no studies address heterogeneous seed identification or classification. For example, several authors have proposed methods to detect or classify types of seeds [ 5 , 6 , 21 ], leaves [ 22 , 23 , 24 ], and crops [ 25 ], to identify the quality of crops [ 26 ] or diseased leaves or crops [ 1 , 25 , 27 , 28 , 29 ], using both traditional and deep learning-based techniques. Table 2 gives a summary of the main methods and findings of the literature.…”

Section: Related Workmentioning

confidence: 99%

“…This growth is mainly due to the fact that computer vision techniques have been combined with deep learning techniques. The latter has offered promising results in various application fields, such as haematology [ 2 ], biology [ 3 , 4 ], or botany [ 5 , 6 ]. Deep learning algorithms differ from traditional machine learning (ML) methods in that they require little or no preprocessing of images and can infer an optimal representation of data from raw images without the need for prior feature selection, resulting in a more objective and less biased process.…”

Section: Introductionmentioning

confidence: 99%

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On the Efficacy of Handcrafted and Deep Features for Seed Image Classification

Loddo

Ruberto

2021

J. Imaging

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Computer vision techniques have become important in agriculture and plant sciences due to their wide variety of applications. In particular, the analysis of seeds can provide meaningful information on their evolution, the history of agriculture, the domestication of plants, and knowledge of diets in ancient times. This work aims to propose an exhaustive comparison of several different types of features in the context of multiclass seed classification, leveraging two public plant seeds data sets to classify their families or species. In detail, we studied possible optimisations of five traditional machine learning classifiers trained with seven different categories of handcrafted features. We also fine-tuned several well-known convolutional neural networks (CNNs) and the recently proposed SeedNet to determine whether and to what extent using their deep features may be advantageous over handcrafted features. The experimental results demonstrated that CNN features are appropriate to the task and representative of the multiclass scenario. In particular, SeedNet achieved a mean F-measure of 96%, at least. Nevertheless, several cases showed satisfactory performance from the handcrafted features to be considered a valid alternative. In detail, we found that the Ensemble strategy combined with all the handcrafted features can achieve 90.93% of mean F-measure, at least, with a considerably lower amount of times. We consider the obtained results an excellent preliminary step towards realising an automatic seeds recognition and classification framework.

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“…Moreover, the validity of the image analysis technique has successfully been applied to discriminate between cultivated plants such as Vitis vinifera ssp. vinifera [ 30 , 31 ], Olea europaea [ 32 ], Malus domestica [ 33 , 34 ], Prunus domestica [ 35 , 36 ], and Cucumis melo [ 37 ].…”

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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Phenotypic identification of plum varieties ( Prunus domestica L.) by endocarps morpho-colorimetric and textural descriptors

Cited by 28 publications

References 35 publications

Molecular and Phenotypic Diversity of Traditional European Plum (Prunus domestica L.) Germplasm of Southern Italy

Molecular and Phenotypic Diversity of Traditional European Plum (Prunus domestica L.) Germplasm of Southern Italy

On the Efficacy of Handcrafted and Deep Features for Seed Image Classification

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

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