基于活动量数据的种公鸡社会等级结构识别方法  

Method for identification of social hierarchy of breeder roosters based on activity intensity

作　　者：王子琪 李丽华[1,2,3] 候旺 周子轩 赵连生 邸梦醉[1] WANG Ziqi;LI Lihua;HOU Wang;ZHOU Zixuan;ZHAO Liansheng;DI Mengzui(College of Mechanical and Electrical Engineering,Hebei Agricultural University,Baoding 071000,China;Key Laboratory of Broiler/Layer Breeding Facilities Engineering,Ministry of Agriculture and Rural Affairs,Baoding 071000,Hebei;Hebei Provincial Key Laboratory of Livestock and Poultry Breeding Intelligent Equipment and New Energy Utilization,Baoding 071000,Hebei;Beijing Animal Husbandry and Veterinary Research Institute,Chinese Academy of Agricultural Sciences,Beijing 100000)

机构地区：[1]河北农业大学机电工程学院,河北保定071000 [2]农业农村部肉蛋鸡养殖设施工程重点实验室,河北保定071000 [3]河北省畜禽养殖智能装备与新能源利用重点实验室,河北保定071000 [4]中国农业科学院北京畜牧兽医研究所,北京100000

出　　处：《河北农业大学学报》2024年第6期97-104,113,共9页Journal of Hebei Agricultural University

基　　金：国家自然科学基金项目(31902209);河北省蛋肉鸡产业技术体系(HBCT2023210204);北京市数字农业创新团队项目资助(BAIC10-2022).

摘　　要：目前本交笼种公鸡的群序确定主要靠人工观察,不仅费时费力且无法自动识别。因此,本研究以本交笼种公鸡为研究对象,提出了1种基于改进灰狼算法(IGWO)结合LGBM的方法,基于活动量数据识别鸡只群序。首先使用九轴惯性传感器获取鸡只行为数据,通过滑动窗口提取合加速度与合角速度44维时域、频域特征,以表征鸡只的活动量信息。引入非线性收敛因子和头狼竞争策略提高灰狼算法的寻优能力,对活动量特征进行降维去冗余处理,提高模型识别效果。结果表明,IGWO-LGBM模型可以准确识别种公鸡的群序,其中,群序识别的精确度、召回率、F1分数平均值分别为84.71%、84.59%、84.57%,模型准确率为84.57%,相比于原始数据集分别提高了3.80%、3.65%、3.67%、3.64%。将降维后的特征作为活动量特征,对活动强度聚类统计并拟合后发现,鸡只群序与高活动量行为占比呈正相关趋势,与低活动量行为占比呈负相关趋势,丰富了群序研究内容。本研究有利于快速识别本交笼种公鸡的群序,为种公鸡群序自动识别提供了一种方法。At present,the determination of the social hierarchy of the natural mating caged breeder roosters mainly relies on manual observation,which is time-consuming and labor-intensive.Therefore,this study took natural mating caged roosters as the research object and proposed a method based on the Improved Gray Wolf Optimization Algorithm(IGWO)combined with LGBM to identify the social hierarchy of chickens based on the activity quantity analysis.Firstly,the nine-axis inertial sensors were used to obtain the chicken behavior data from the sliding window extracted the chicken’s activity information,that was characterized by the combined acceleration and combined angular velocity of totaling 44-dimensional time-domain and frequency-domain features,which characterize.The nonlinear convergence factor and the competition strategy of the first wolf were introduced to improve the optimization ability of the Gray Wolf Optimization Algorithm,and dimensionality reduction of activity features was performed to reduce redundancy and improve the model recognition effect.The results showed that the IGWO-LGBM model accurately identified the social hierarchy of chickens.Among them,the precision,recall,and F1-score of social hierarchy recognition were 84.71%,84.59%,and 84.57%,respectively,and the model accuracy was 84.57%,which were improved by 3.80%,3.65%,3.67%,and 3.64%,respectively,compared with the original dataset.The features selected after dimensionality reduction were used as the activity features.After clustering statistics according to activity intensity and fitting by the least squares method,it was found that the chicken social hierarchy was positively correlated with the proportion of high activity behavior and negatively correlated with the proportion of low activity behavior,which enriched the research on social hierarchy.This study is helpful for rapid identification of the social hierarchy of breeder roosters and provides a method for automatic identification of the social hierarchy of breeder roosters.

关 键 词：本交笼种鸡 群序识别 活动量 惯性传感器 灰狼优化算法 

分 类 号：S24[农业科学—农业电气化与自动化] S831[农业科学—农业工程]