Classification of goat genetic resources using morphological traits. Comparison of machine learning techniques with linear discriminant analysis

Serrano, Evangelina Rodero; González, Ana M.; Dorado-Moreno, Manuel; Luque, Manuel Melendo; Hervás, César

doi:10.1016/j.livsci.2015.06.028

Cited by 4 publications

(2 citation statements)

References 32 publications

(48 reference statements)

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“…There has been an increasing interest in the characterization of Spanish dairy sheep breeds, especially toward the development of quality marks such as PDO and PGI (Gaspar et al, 2011). In recent years several authors have focused on morphological variability to characterize different small ruminant breeds (Carneiro et al, 2010;Rodero et al, 2015;Mavule et al, 2016). However, so far, no studies have discriminated between dairy breeds focusing on milk compositional or technological quality.…”

Section: Discussionmentioning

confidence: 99%

Multivariate analysis of the milk coagulation process in ovine breeds from Spain

Caballero-Villalobos

Figueroa

Xibrraku

et al. 2018

Journal of Dairy Science

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In Spain, ewe milk is mainly used for cheesemaking, and farming systems have traditionally been based on the use of autochthonous breeds. However, in recent years, the progressive introduction of highly productive foreign breeds in Spanish farms has led to an increasing interest in the characterization of dairy sheep breeds to evaluate whether genetic selection schemes should focus on productivity or milk technological aptitude. The purpose of this work was to explore milk composition and coagulation to classify 4 of the main dairy sheep breeds used in Spain. This study included 832 individual ewe milk samples from the breeds Manchega, Assaf, Merino de Grazalema, and Merino de Los Pedroches. Samples were analyzed for native pH, composition (fat, protein, lactose, and total solids), coagulation properties, and individual laboratory curd yield. An indicator of coagulation efficiency was also determined. Canonical discriminant analysis was performed to establish differences and similarities among breeds based on the measured variables. In addition, cluster analysis was performed to study and quantify the concrete relationships among the discriminated groups. Discriminant analysis proved to be a powerful tool to accurately draw distinctions between breeds. In all cases, discrimination among breeds was evident and the 4 breeds could be easily differentiated. Cluster analysis showed greater similarity between Merino de Grazalema and Assaf compared with the other breeds, and F-statistics indicated a higher discriminating ability for the variables related to milk composition. However, Merino de Grazalema and Manchega were difficult to separate according to milk composition, but the coagulation process differenced them clearly. Coagulation also evidenced similarities between Manchega and Merino de Los Pedroches, although the latter was revealed to be the most different breed of all 4, which could lay the ground for its differentiation as an independent breed in the Official Catalogue of Spanish Livestock Breeds.

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Section: Discussionmentioning

confidence: 99%

Multivariate analysis of the milk coagulation process in ovine breeds from Spain

Caballero-Villalobos

Figueroa

Xibrraku

et al. 2018

Journal of Dairy Science

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“…The research was performed with two different types of the dataset (i.e., full sheep images and cropped facial images) and after fine‐tuning of last 6 VGG layers for 10 epochs, maximum accuracy was found to be above 95%. In Reference 78, a hierarchical model was proposed to classify the 12 Spanish goat species using 9 morphological traits and 3 aptitudes. The first hierarchy level of the model uses KNN/MLP for classifying the goats based on aptitude only and further in the next hierarchy level breeds were examined again using three new KNN/MLP classifiers one for each aptitude (meat, milk & dual purpose).…”

Section: Related Workmentioning

confidence: 99%

Contemporary machine learning applications in agriculture: Quo Vadis?

Mahmood

Tiwari

Singh

et al. 2022

Concurrency and Computation

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Agricultural automation is an emerging subject today to accomplish the food demands of individuals across the globe. Machine learning is one such agricultural automation tool that has been adopted briskly in the recent decade due to its ability to process countless input data and handle non-linear tasks. Availability and continuous development of agricultural data led the machine learning pervasive in multiple aspects of agriculture. This paper systematically analyses and summarizes the 81 quality research efforts published in the past decade dedicated to the various contemporary machine learning applications in agriculture and food production systems. We examined and categorized each agricultural problem under study into four categories and each category into its subcategories. The finding demonstrates the contemporary applications of machine learning in broad agricultural subcategories and determines where it is heading shortly; based upon contributions of researchers, utilization of machine learning models/algorithms, and the availability of agricultural datasets.Through the analysis, it is discovered that the current innovation can help the improvement of agricultural automation to accomplish the advantages of minimal cost, high efficiency, and better precision. This paper can serve as an investigatory guide for researchers, academicians, engineers, and manufacturers to understand and apply modern and upgraded cognitive technologies to each subcategory of the agricultural sector.

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Exploring the genetic diversity and relationships between Spanish and Moroccan goats using microsatellite markers

Moutchou

González

Chentouf³

et al. 2018

Small Ruminant Research

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Classification of goat genetic resources using morphological traits. Comparison of machine learning techniques with linear discriminant analysis

Cited by 4 publications

References 32 publications

Multivariate analysis of the milk coagulation process in ovine breeds from Spain

Multivariate analysis of the milk coagulation process in ovine breeds from Spain

Contemporary machine learning applications in agriculture: Quo Vadis?

Exploring the genetic diversity and relationships between Spanish and Moroccan goats using microsatellite markers

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