Unsupervised delineation of chicken eggs based on the universal avian egg model
Year: 2025
Type: article
Abstract: Due to advancements in computing technology, egg image processing has become an industrial standard in poultry research. It has been utilized at various stages of chicken breeding and hatchery, as well as egg quality assessment and control. Thus far, existing methods are impeded by the need for problem specific provisions, whose inherent setting is counterintuitive, or those involving overly complex artificial neural networks. Motivated by the latest development of universal avian egg models, this paper proposes an automated egg delineation method, based on expectation-maximization framework. Accordingly, only a few model and pose parameters were required, discarding the need for large, annotated training dataset or excessive priors. Moreover, numerical treatments were also implemented to ensure its stability in practice. The experiments on a public dataset demonstrate that the method could segment eggs of various shapes and sizes and be acquired under different perspectives and lighting conditions. Reported performance analyses revealed the average dice coefficient and intersection of union of 0.9782 and 0.9575, respectively. Its resilience to noise and partial occlusion were also evaluated. Thus, the proposed method not only enhances this critical task, but is also expected to be integrable with existing egg image recognition workflows in precision farming and food inspection.
Institution: Suranaree University of Technology
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $2,530
OpenAlex ID: W4411146473