Computer Vision for Morphometric Evaluation of Broiler Chicken Bones

Abstract

Locomotor problems are a challenge for commercial poultry, but current methods used to assess the bone structure of chickens are few and laborious. The objective of this study is to present software for the automatic extraction of morphometric characteristics of broiler chicken's locomotor bones throughout the life cycle, by applying computer vision techniques. 112 samples from the tibia and 112 from the femur of commercial chickens were used, subdivided by age (0, 7, 14, 21, 28, 35, and 42 days). The images were digitally processed to extract bone morphometric properties (area, length, and perimeter). New software was created, including the proposed processing and algorithms for obtaining the morphometric characteristics. Classification models (artificial neural networks, ANN, and k-nearest neighbors' algorithm, KNN) were developed to classify bones according to age and type. The results of the software were satisfactory, the sample bank could be handled correctly, a high applicability to test images from other sources was determined. For the classification of bones, the ANN method was more accurate than KNN. The information obtained in this study opens new possibilities for evaluative studies of broiler locomotive systems.