Chicken Weight Estimation Using Deep Learning

In this work, we utilized deep learning models for depth image regression to predict chicken weights. The dataset consists of annotated 99,427 depth images obtained from 18,706 chickens standing on the weighing scale during the rearing days 21-84. Pretrained models performed regression on the depth image data, including Mobilenet V2, ResNet50 V2, ResNet101 V2, ResNet152 V2, InceptionV3, and Xception. All models performed comparable results regarding mean absolute error ( MAE) and mean relative error (MRE); however, Xception performed best with an MAE of 17.2 g and an MRE of 2.52% on the test dataset compared to the reference weight. Based on these results, chicken weight estimation using depth images and deep learning is a promising technique for daily growth rate monitoring for the poultry industry.