Human counting versus artificial intelligence for assessing medullation in mohair fibres
The fleeces of mammals with dense coats, such as the mohair fleeces of Angora goats, usually include medullated fibres. These fibres constitute a problem for the textile industry because of their structural characteristics. Three experiments were conducted in this study, with the aim of comparing h...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
South African Society for Animal Science
2025-05-01
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| Series: | South African Journal of Animal Science |
| Subjects: | |
| Online Access: | https://www.sajas.co.za/article/view/23318 |
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| Summary: | The fleeces of mammals with dense coats, such as the mohair fleeces of Angora goats, usually include medullated fibres. These fibres constitute a problem for the textile industry because of their structural characteristics. Three experiments were conducted in this study, with the aim of comparing human image analysis to digital image analysis and artificial intelligence (AI), in terms of their ability to determine the incidence of medullation in mohair samples. The experiments entailed determining the incidences of industry non-objectionable medullated (NOB) fibres and objectionable medullated (SME) fibres, as percentages of the non-medullated fibres. In each experiment, a set of samples was analysed by both laboratory personnel and by different AI models using a Smart Fiber Medullometer. Laboratory personnel showed better coincidence and higher correlations with the AI models when counting SME fibres (r = 0.64–0.97) than when counting NOB fibres (r = 0.57–0.87). This could be the result of the more clearly defined characteristics of SME fibres, in relation to NOB fibres. The results of this study indicate a great advance in the automatic detection of SME and NOB fibres in mohair samples. However, further adjustments of the AI models are required for counting NOB fibres.
Submitted 12 June 2024; Accepted 8 April 2025; Published May 2025
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Significance of research to South African science
The article holds notable significance for South African science, particularly within the textile and agricultural sectors. Mohair is a valuable fibre in South Africa, which is one of the world’s leading producers. The study compares human assessment with artificial intelligence (AI) methods for detecting medullated fibres in mohair, a critical quality determinant for the textile industry. The findings demonstrate the growing potential of AI technologies to enhance the accuracy and efficiency of fibre quality analysis, offering a pathway to modernising mohair processing and improving global competitiveness. This aligns with South Africa's strategic focus on smart agriculture, innovation, and adding value to animal fibre production.
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| ISSN: | 0375-1589 2221-4062 |