An overview on Taxonomic Keys and Automated Species Identification (ASI)

De, Mitu; Dey, Santi Ranjan

doi:10.52756/ijerr.2019.v20.004

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…The correct taxonomic identification of species forms the foundation for biodiversity, ecology, phylogeny, and conservation studies. Traditionally, species identification has relied on the use of dichotomous keys based on morphological characters ( Griffing, 2001 ; De & Dey, 2019 ). Despite the advent of DNA barcoding, morphological identification remains prevalent, primarily due to the limitations of DNA reference databases ( Blaxter, 2004 ; Valentini, Pompanon & Taberlet, 2009 ; Guo et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Machine learning algorithms accurately identify free-living marine nematode species

Brito de Jesus,

Vieira,

Gheller

et al. 2023

PeerJ

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Background Identifying species, particularly small metazoans, remains a daunting challenge and the phylum Nematoda is no exception. Typically, nematode species are differentiated based on morphometry and the presence or absence of certain characters. However, recent advances in artificial intelligence, particularly machine learning (ML) algorithms, offer promising solutions for automating species identification, mostly in taxonomically complex groups. By training ML models with extensive datasets of accurately identified specimens, the models can learn to recognize patterns in nematodes’ morphological and morphometric features. This enables them to make precise identifications of newly encountered individuals. Implementing ML algorithms can improve the speed and accuracy of species identification and allow researchers to efficiently process vast amounts of data. Furthermore, it empowers non-taxonomists to make reliable identifications. The objective of this study is to evaluate the performance of ML algorithms in identifying species of free-living marine nematodes, focusing on two well-known genera: Acantholaimus Allgén, 1933 and Sabatieria Rouville, 1903. Methods A total of 40 species of Acantholaimus and 60 species of Sabatieria were considered. The measurements and identifications were obtained from the original publications of species for both genera, this compilation included information regarding the presence or absence of specific characters, as well as morphometric data. To assess the performance of the species identification four ML algorithms were employed: Random Forest (RF), Stochastic Gradient Boosting (SGBoost), Support Vector Machine (SVM) with both linear and radial kernels, and K-nearest neighbor (KNN) algorithms. Results For both genera, the random forest (RF) algorithm demonstrated the highest accuracy in correctly classifying specimens into their respective species, achieving an accuracy rate of 93% for Acantholaimus and 100% for Sabatieria, only a single individual from Acantholaimus of the test data was misclassified. Conclusion These results highlight the overall effectiveness of ML algorithms in species identification. Moreover, it demonstrates that the identification of marine nematodes can be automated, optimizing biodiversity and ecological studies, as well as turning species identification more accessible, efficient, and scalable. Ultimately it will contribute to our understanding and conservation of biodiversity.

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Section: Introductionmentioning

confidence: 99%

Machine learning algorithms accurately identify free-living marine nematode species

Brito de Jesus,

Vieira,

Gheller

et al. 2023

PeerJ

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Machine Learning Approach to Support Taxonomic Discrimination of Mayflies Species Based on Morphologic Data

Marulanda Lopez,

de Brito Neto,

dos Santos Ferreira

2024

Neotrop Entomol

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Numerical Taxonomic analysis for the estimation of Genetic Diversity among some traditional rice (Oryza sativa L.) varieties of West Bengal

Saha¹,

Dey²

2022

IJERR

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The use of numerical taxonomy in plant systematics is crucial because it allows for the improvement of traditional taxonomic data gathered from sources like as morphology, physiology, chemistry, anatomy, and cytology, among others, by utilizing a better and greater number of documented features. It has been determined that it is crucial to keep track of crop genetic resource management dynamics using the fundamental diversity data of traditional crop varieties or landraces. The protection of rice diversity and the use of diversity for rice breeding are strongly tied to the food security of this region because rice is the most significant staple food in South-East Asia. Traditional rice types continue to grow in many indigenous societies despite the relentless advance of modernization, which includes industrialization and land use rules focused on the market. Folk rice varieties are also grown in various areas of West Bengal. The genetic diversity among these traditional or folk types is the only source for future rice enhancement. In this study, many local rice varieties were gathered from various regions of West Bengal, and "richness data" was calculated utilizing this knowledge. Additional data on agro-morphological traits revealed considerable variations between genotypes. Wide variance among the folk rice genotypes was revealed by estimating mean, range, standard deviation, standard error, and coefficient of variation. Following numerical taxonomic analysis, classification of genotypes revealed high genetic diversity among West Bengal's traditional rice varieties. This genetic variety might be utilised in rice breeding, where it is challenging to get access to new technology.

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An overview on Taxonomic Keys and Automated Species Identification (ASI)

Cited by 3 publications

References 17 publications

Machine learning algorithms accurately identify free-living marine nematode species

Machine learning algorithms accurately identify free-living marine nematode species

Machine Learning Approach to Support Taxonomic Discrimination of Mayflies Species Based on Morphologic Data

Numerical Taxonomic analysis for the estimation of Genetic Diversity among some traditional rice (Oryza sativa L.) varieties of West Bengal

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