Coupling Artificial Intelligence with Proper Mathematical Algorithms to Gain Deeper Insights into the Biology of Birds’ Eggs

Animals, Journal Year: 2025, Volume and Issue: 15(3), P. 292 - 292

Published: Jan. 21, 2025

Avian eggs are products of consumer demand, with modern methodologies for their morphometric analysis used improving quality, productivity and marketability. Such studies open up numerous prospects the introduction artificial intelligence (AI) deep learning (DL). We first consider state art DL in poultry industry, e.g., image recognition applications detection egg cracks, content freshness. comment on how algorithms need to be properly trained ask what information can gleaned from shape. Considering geometry profiles, we revisit Preston–Biggins model, Hügelschäffer’s universal models, principles universalism “The Main Axiom”, proposing a series postulates evaluate legitimacy practical application various mathematical models. stress that different models have pros cons, using them combination may yield more useful results than individual use. classic shape index alongside other alternatives, drawing conclusions about importance indices context applying going forward. Examining weight, volume, surface area air cell calculations, might applied, storage. The value is pre-incubation sorting, optimization storage periods incubation regimes, representation dimensional characteristics. Each thus combined provide synergy threshold many scientific discoveries, technological achievements industrial successes facilitated through AI DL.

Language: Английский

Enhancing Egg Grading Precision through AI and Computer Vision-Powered Morphometric Analysis

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 27, 2025

Egg size determination is an important activity in the poultry industry. Traditional methods of assessment are labour-intensive, error-prone, and time-consuming. Machine learning proving to be a major game changer all sectors this has potential upgrade automate egg grading as well. Considering this, research was undertaken evaluate AI-driven computer vision approaches for extraction dimensions from 2D images. The images were annotated saved OBB dataset appropriately preprocessed. Yolo11 models evaluated their ability detect eggs yolo11x-obb model found optimal study. Mean Absolute Errors (MAE) two final predictive 0.28 length 0.19 breadth, with Pearson Correlations 0.81 0.85 breadth Model 1 MAEs 0.29 0.26 0.88 2. Science4ImpactStatement: This foundational concludes that Learning can predict good accuracy could potentially used breeding, automatic grading, packaging, processing well marketing.

Language: Английский