Lameness prediction in broiler chicken using a machine learning technique

Abstract

Broiler flock welfare is usually assessed through mortality, physiology, behavior, and walking ability. The possibility of assessing broiler chicken lameness using the bird walking ability was investigated using the machine learning approach for the first time. Data on broiler walking speed and acceleration, genetic strain, and sex were recorded and input in a dataset. Broilers were classified according to the 6-point gait score (GS0 is a sound bird, and GS5 is a severely lame bird). Decision trees were built initially using all datasets. The confusion matrix of each developed model was analyzed. The pruning technique was used, removing from the dataset the variables that did not infer in the classification results. We reorganized the dataset and re-arranged the data by grouping the intermediate target class of gait score using the Borda Count method. Re-processing data, we obtained a new set of decision trees. Using the 3-point gait score (GS0 is a sound bird, and GS2 is a lame bird), we obtained a new model with better accuracy (78%); however, the model had a lower accuracy for classifying lame broilers (GS2, 5%). The final decision tree was selected for classifying broilers, either sound or lame, according to their walking speed. The developed model presented good accuracy (91%), and it ordered properly sound (86%) and lame birds (92%). The novel model might be used to assess broiler lameness on-farm by registering the bird displacement velocity. Further developments using the model might allow flock lameness detection automatically.