Automated egg grading system using computer vision: Investigation on weight measure versus shape parameters

Nasir, Ahmad Fakhri Ab.; Sabarudin, Siti Suhaila; Majeed, Anwar P. P. Abdul; Ghani, Ahmad Shahrizan Abdul

doi:10.1088/1757-899x/342/1/012003

Cited by 9 publications

(2 citation statements)

References 5 publications

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“…Egg grading is an essential operation in the production of commercial eggs to control egg quality. In Malaysia, the common egg grading parameters used by the industries are based on weights as listed in Table 1 [2].…”

Section: Introductionmentioning

confidence: 99%

Vision-based Egg Grading System using Support Vector Machine

Lim,

Kang Lai,

Lim

et al. 2024

IJORAS

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Being known as a nutrient-dense food, eggs are high in demand in the marketplace and high-quality eggs are much sought-after. Hence, egg grading is in place to sort eggs into different grades. Experienced graders are required for their knowledge to classify egg grades and as humans are involved, errors when performing manual grading are unavoidable. This study aims to develop a vision-based egg classification system that requires minimal human intervention. The proposed system houses a camera to acquire real-time images of the eggs and these images are served as the input to the algorithm. Based on the 6 geometrical features derived from the geometric parameters of the egg image, the eggs are classified using Support Vector Machine (SVM). The experiment results show the proposed egg grading system with a linear kernel SVM model can yield as high as 92.59% training accuracy.

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Section: Introductionmentioning

confidence: 99%

Vision-based Egg Grading System using Support Vector Machine

Lim,

Kang Lai,

Lim

et al. 2024

IJORAS

View full text Add to dashboard Cite

show abstract

“…In the same year, Ang et al proposed a method combining robots to statistically count and collect eggs in free-range chicken farms, with a positioning error within 2 cm [10]. In 2018, Ab Nasir A et al designed an automatic egg-grading system with positioning and recognition accuracies exceeding 95% [11]. In 2023, Li et al used an improved YOLOv7 network (MobileOne-YOLO) for detecting fertilized duck eggs, significantly improving FPS performance by 41.6% while maintaining the same accuracy as YOLOv7 [12].…”

Section: Introductionmentioning

confidence: 99%

Improved YOLOv8 Model for Lightweight Pigeon Egg Detection

Jiang,

Zhou,

Xie

et al. 2024

Animals

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In response to the high breakage rate of pigeon eggs and the significant labor costs associated with egg-producing pigeon farming, this study proposes an improved YOLOv8-PG (real versus fake pigeon egg detection) model based on YOLOv8n. Specifically, the Bottleneck in the C2f module of the YOLOv8n backbone network and neck network are replaced with Fasternet-EMA Block and Fasternet Block, respectively. The Fasternet Block is designed based on PConv (Partial Convolution) to reduce model parameter count and computational load efficiently. Furthermore, the incorporation of the EMA (Efficient Multi-scale Attention) mechanism helps mitigate interference from complex environments on pigeon-egg feature-extraction capabilities. Additionally, Dysample, an ultra-lightweight and effective upsampler, is introduced into the neck network to further enhance performance with lower computational overhead. Finally, the EXPMA (exponential moving average) concept is employed to optimize the SlideLoss and propose the EMASlideLoss classification loss function, addressing the issue of imbalanced data samples and enhancing the model’s robustness. The experimental results showed that the F1-score, mAP50-95, and mAP75 of YOLOv8-PG increased by 0.76%, 1.56%, and 4.45%, respectively, compared with the baseline YOLOv8n model. Moreover, the model’s parameter count and computational load are reduced by 24.69% and 22.89%, respectively. Compared to detection models such as Faster R-CNN, YOLOv5s, YOLOv7, and YOLOv8s, YOLOv8-PG exhibits superior performance. Additionally, the reduction in parameter count and computational load contributes to lowering the model deployment costs and facilitates its implementation on mobile robotic platforms.

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