Automated precision weighing: Leveraging 2D video feature analysis and machine learning for live body weight estimation of broiler chickens

• DOI: 10.1016/j.atech.2025.100793
• Publication Date: 2025-01-19T15:22:02Z
• Authors: Mairead Campbell; Paul Miller; Katerine Díaz-Chito; Sean Irvine; Mary Baxter; Jesús Martínez De...; Xin Hong; Niall McLaughlin; Thianantha Arumugam; Niamh O'Connell
• Keywords: HD9000-9495; Automated precision weighing; Broiler; Agriculture (General); bird live weight; 600; Agricultural industries; Weight; 630; production cycle; S1-972; 2-dimensional feature descriptor; Computer vision; Regression analysis; broiler chickens

The measurement of bird live weight during the production cycle is an important management practice in commercial broiler farming. However, the accuracy and practicalities of current weighing methods are limited. This paper proposes a non-invasive system that uses low-cost, overhead cameras combined with computer vision and AI techniques to automatically weigh broiler chickens. The main objectives were to: (i) evaluate 2D video feature descriptors, together with regression modelling, to predict the live weight of broilers; (ii) establish the impact of posture (i.e. sitting/standing) and bird age on the accuracy of weight estimation; (iii) assess the feasibility of the camera-weighing system to monitor weight at different bird ages. In the first experiment, a video feature analysis was performed to evaluate the accuracy of 2D feature descriptors (ellipse axes, ellipse area, bounding box width, bounding box height) to predict the weight of broilers. Individual birds were manually weighed to establish a reference weight. The relationship between the feature sets and the reference weight was evaluated using six multivariate regression models. The approach was tested on two groups of broilers aged 23 (n = 21 broilers) and 35 (n = 23 broilers) days old, weighing between 570 and 2980 g. In experiment 2, the best performing feature set and linear regression modelling from experiment 1 were applied to a larger number of birds across a greater age range (5 to 35 days old, n = 222 broilers). To be more representative of the intended application of this technology, footage was recorded from the feeding area of a commercial broiler house and an automated chicken detector and tracking method was applied. The model was retrained using reference weights from experiment 2 (ranging from 100 to 3085 g) to refine model performance. In experiment 1, the posture feature did not improve weight estimation whilst age improved the performance of all models. The accuracy of body weight estimation was greatest when bird age and the minor ellipse axis (x,y endpoints of the maximum points that are perpendicular to the longest line that can be drawn through an object) were used as model features. In experiment 2, the model showed the poorest performance in 5-day old birds with a mean relative error of 12.1 ± 7.9 %. Overall, the model could estimate the weight of a broiler chicken with a mean relative error of 7.0 ± 5.8 %. The results indicate that the analysis of 2D image features using video analytics and regression modelling is a promising method of obtaining rapid, cost-effective and accurate estimates of broiler live weight.