IEEE Access (Jan 2024)
A Recognition Method for Aggressive Chicken Behavior Based on Machine Learning
Abstract
Aggressive behavior is an important indicator of chicken welfare assessment. At present, the aggressive behavior of chickens typically requires human observation for welfare assessment, and the assessment results are influenced by the subjective judgment of humans. This paper proposes an aggressive chicken behavior identification method based on a hybrid strategy improved Sparrow Search Algorithm combined with Support Vector Machine (ISSA-SVM). Nine-axis inertial sensors were used to collect the behavioral data of chickens. A total of 231-dimensional feature data in the time and frequency domains of the behavioral data were extracted through a 1 s sliding window. To reduce feature redundancy, the initial population is initialized using circle chaotic mapping instead of random initialization of the original sparrow algorithm to increase the uniformity of the initial population distribution in the feature space; adaptive weights are introduced to increase the search range of the early iteration, and the global optimal solution of the previous generation is introduced to improve the global search capability of the algorithm; the optimal solution is perturbed using the dimension-by-dimension mutation strategy of adaptive t-distribution to increase the diversity of the feature distribution. ISSA-SVM reduced the feature dimensionality from 231 to 17, indicating a reduction of 92.6%. The recognition overall accuracy of ISSA-SVM for aggressive chicken behavior was 94.27%, which improved by 1.33% compared to SVM. The results of the experiment show that of all the aggressive chicken behaviors during the 5-day experiment, fighting behavior occurred most frequently from 11:00 to 13:00 and from 17:00 to 18:00. This study provides a method for the automatic identification of aggressive chicken behavior and can serve as an informational tool for poultry welfare assessment.
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