利用机器学习构建和优化早期体尺性状对成年母鸡腹脂沉积的预测模型

doi:10.11843/j.issn.0366-6964.2025.02.007

摘要/Abstract

摘要：

为探究活体、无创、简单、高效的母鸡腹脂沉积性状测定和选育技术方法，本研究以清远麻鸡为研究对象，将多体尺性状选择法与8种机器学习模型相结合，分别构建不同日龄体尺性状对母鸡腹脂含量的回归预测模型和分类预测模型。利用58~136日龄间各个日龄的多个早期体尺性状结合机器学习方法，体尺测定日龄对预测成年清远麻母鸡腹脂含量的准确性未表现出明显差异；进行回归预测时，RF模型的预测效果最好，拟合效果R²为0.821~0.861，预测误差MAE为6.32~7.27；进行分类预测时，Bagging模型在二分类、三分类中均具有更高的预测准确度，二分类准确度ACC可达94.54%~100%，三分类准确度ACC可达99.58%~100%。本研究基于机器学习建立并优化了优质鸡腹脂沉积活体预测模型，能够为优质鸡腹脂早期活体选育等奠定技术基础，也为腹脂含量预测模型构建的相关技术探索提供参考。

关键词: 鸡, 腹脂沉积, 早期体尺性状, 机器学习, 预测模型

Abstract:

In order to investigate a live, non-invasive, simple, and efficient method for the assessment of abdominal fat deposition traits and selection of hens, the present study took Qingyuan partridge chickens as research object and combined the multiple body size trait selection method with eight machine learning models to construct regression prediction models and classification prediction models for the abdominal fat deposition of hens at different days of age. Using multiple early body size traits at various ages between 58 and 136 days of age combined with machine learning methods, the accuracy of body size traits at different ages in predicting abdominal fat content of adult Qingyuan partridge hens did not show significant differences. The RF model had the best prediction effect for regression prediction, with a fitting effect of R² of 0.821-0.861 and a prediction error MAE of 6.32-7.27. In terms of classification prediction, the Bagging model exhibited superior performance in both binary and tertiary classification. The binary classification accuracy ACC reached 94.54% to 100%, while the tertiary classification accuracy ACC reached 99.58% to 100%. In this study, live prediction models for abdominal fat deposition in high-quality chickens were established and optimized based on machine learning. These models can serve as a technical foundation for early live selection on abdominal fat deposition and breeding of high-quality chickens as well as for the exploration of the related technology for the construction of prediction models for abdominal fat content.

Key words: chicken, abdominal fat deposition, early body size traits, machine learning, prediction model

中图分类号:

S831.2

杨苗苗, 谢莉, 简宝怡, 罗超维, 谢卓君, 朱飘, 周天日, 李华, 向海. 利用机器学习构建和优化早期体尺性状对成年母鸡腹脂沉积的预测模型[J]. 畜牧兽医学报, 2025, 56(2): 548-558.

YANG Miaomiao, XIE Li, JIAN Baoyi, LUO Chaowei, XIE Zhuojun, ZHU Piao, ZHOU Tianri, LI Hua, XIANG Hai. Construction and Optimization of Prediction Models for Abdominal Fat Deposition in Adult Hens based on Early Body Size Traits using Machine Learning[J]. Acta Veterinaria et Zootechnica Sinica, 2025, 56(2): 548-558.

图/表 7

表 1

表 2

表 3

表 4

图 1

表 5

基于不同日龄特征选择后的体尺性状的回归预测"

日龄/d Age	模型 Model	决策树特征选择 DT feature selection			随机森林特征选择 RF feature selection			梯度提升决策树特征选择 GBDT feature selection			梯度提升决策树特征选择 XGBoost feature selection
日龄/d Age	模型 Model	决定系数 R²	平均绝对误差 MAE	时间/s Time	决定系数 R²	平均绝对误差 MAE	时间/s Time	决定系数 R²	平均绝对误差 MAE	时间/s Time	决定系数 R²	平均绝对误差 MAE	时间/s Time
58	DT	0.038	17.15	0.73	0.038	17.2	0.83	0.038	17.15	0.67	0.032	17.48	0.85
	RF	0.839	6.92	2.12	0.821	7.27	2.37	0.846	6.74	2.67	0.837	7.09	2.48
	GBDT	0.748	8.64	6.15	0.773	8.44	6.02	0.730	9.09	5.83	0.673	10.11	6.31
	XGBoost	0.771	7.91	1.63	0.771	7.93	2.43	0.744	8.52	1.62	0.771	8.11	2.04
72	DT	0.044	16.85	0.57	0.044	16.85	0.73	0.044	16.85	0.63	0.020	17.23	0.97
	RF	0.842	6.96	1.98	0.842	6.96	2.28	0.846	6.86	2.14	0.846	6.80	2.37
	GBDT	0.732	9.15	6.42	0.732	9.15	7.34	0.728	9.04	6.39	0.734	9.11	6.72
	XGBoost	0.731	8.63	1.49	0.731	8.63	1.59	0.706	8.81	1.61	0.680	9.27	1.7
86	DT	0.060	17.10	0.70	0.060	17.10	1.00	0.060	17.10	0.64	0.060	17.10	0.91
	RF	0.854	6.63	2.08	0.845	6.83	2.22	0.845	6.83	2.20	0.839	6.8	2.31
	GBDT	0.750	8.76	6.16	0.760	8.61	6.87	0.760	8.61	8.09	0.764	8.53	6.39
	XGBoost	0.708	9.16	1.63	0.692	9.36	1.67	0.692	9.36	1.66	0.768	8.26	1.77
104	DT	0.068	17.08	0.61	0.068	17.08	0.78	0.068	17.08	0.62	0.061	16.88	0.91
	RF	0.852	6.49	1.98	0.851	6.54	2.23	0.851	6.54	1.97	0.827	6.60	2.20
	GBDT	0.774	8.30	6.40	0.774	8.30	6.37	0.774	8.30	6.25	0.726	8.84	6.10
	XGBoost	0.754	8.17	8.17	0.754	8.17	1.74	0.754	8.17	1.63	0.752	7.99	1.85
119	DT	0.057	17.18	0.61	0.057	17.18	0.75	0.057	17.18	0.63	0.042	17.25	0.90
	RF	0.857	6.55	2.21	0.855	6.56	2.14	0.854	6.59	2.06	0.851	6.51	2.26
	GBDT	0.776	8.09	6.00	0.776	8.09	6.56	0.776	8.09	6.82	0.745	8.48	6.39
	XGBoost	0.765	8.34	1.63	0.765	8.34	2.00	0.765	8.34	1.84	0.736	8.49	1.98
136	DT	0.121	16.64	0.62	0.121	16.64	1.37	0.121	16.64	0.69	0.121	16.64	1.75
	RF	0.843	6.58	2.47	0.853	6.65	2.37	0.846	6.52	2.24	0.861	6.32	2.39
	GBDT	0.776	7.96	6.45	0.771	8.24	6.34	0.776	7.96	7.24	0.768	8.41	5.89
	XGBoost	0.792	7.69	1.57	0.777	7.93	2.76	0.792	7.69	1.12	0.782	7.61	1.74

表 5

表 6

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日龄/d Age	项目 Item	体重/g Weight	冠高/cm Comb height	体斜长/cm Body diagonal length	龙骨长/cm Keel length	胫长/cm Shank length	胫围/cm Shank circumference	腰围/cm Waist circumference	胸宽/mm Chest width	胸深/mm Chest depth	骨盆宽/mm Pelvic width
58	平均值±标准差	746.55±83.01	0.79±0.24	16.45±1.28	7.94±0.62	6.28±0.73	3.23±0.22	26.51±1.78	47.54±4.92	69.3±5.23	39.11±3.35
58	相关系数	0.17	0.10	0.15	0.11	0.03	0.04	0.02	0.06	0.00	0.05
72	平均值±标准差	946.1±97.44	0.94±0.29	18.17±1.06	9.35±0.75	7.11±0.45	3.31±0.18	30.2±1.73	51.44±3.54	68.62±3.88	40.83±2.89
72	相关系数	0.18	0.09	0.09	0.03	0.02	0.06	0.12	0.03	0.02	0.02
86	平均值±标准差	1 088.03±111.21	1.08±0.46	19.48±0.97	10.4±0.8	7.25±0.39	3.37±0.21	32.69±1.71	57.64±3.46	70.63±3.68	45.1±2.87
86	相关系数	0.24	0.02	0.15	0.07	0.05	0.02	0.22	0.12	0.14	0.04
104	平均值±标准差	1 214.12±116.96	1.56±0.59	19.9±1.18	11.02±0.61	7.36±0.32	3.52±0.15	33.48±1.71	59.66±4.01	75.2±4.63	49.34±2.5
104	相关系数	0.26	0.11	0.17	0.06	0.19	0.16	0.21	0.07	0.15	0.10
119	平均值±标准差	1 413.07±142.67	1.93±0.64	20.76±1.15	11.86±0.65	7.86±0.43	3.72±0.18	35.81±1.81	65.46±3.38	81.44±4.27	53.31±3.08
119	相关系数	0.27	0.13	0.16	0.05	0.07	0.15	0.21	0.04	0.11	-0.08
136	平均值±标准差	1 694.13±177.83	2.42±0.66	20.86±1.00	11.93±0.64	7.92±0.45	3.71±0.16	38.07±1.91	68.07±4.81	97.93±5.02	56.44±3.63
136	相关系数	0.38	0.01	0.14	0.04	0.00	0.07	0.23	0.14	0.13	0.03

日龄/d Age	指标 Metric	第一特征 First feature	第二特征 Second feature	第三特征 Third feature	第四特征 Fourth feature	第五特征 Fifth feature
58	DT	胸宽	骨盆宽	胸深	体斜长	冠高
	RF	骨盆宽	胸深	胸宽	体斜长	体重
	GBDT	胸深	胸宽	骨盆宽	体斜长	腰围
	XGBoost	胸宽	骨盆宽	腰围	胫长	胫围
72	DT	体重	骨盆宽	腰围	胸宽	胸深
	RF	骨盆宽	胸深	胸宽	体重	腰围
	GBDT	骨盆宽	胸宽	胸深	腰围	龙骨长
	XGBoost	骨盆宽	胸宽	胫长	龙骨长	胸深
86	DT	骨盆宽	腰围	龙骨长	胸深	胸宽
	RF	骨盆宽	胸宽	腰围	胸深	体重
	GBDT	骨盆宽	胸宽	腰围	胸深	体重
	XGBoost	骨盆宽	胸宽	腰围	胸深	胫长
104	DT	体重	腰围	骨盆宽	胸深	胸宽
	RF	体重	腰围	胸深	骨盆宽	胸宽
	GBDT	体重	腰围	胸深	骨盆宽	胸宽
	XGBoost	胫长	腰围	胸宽	骨盆宽	胸深
119	DT	骨盆宽	体重	胸深	胸宽	胫长
	RF	体重	胸深	骨盆宽	胸宽	胫长
	GBDT	胸宽	体重	胸深	骨盆宽	胫长
	XGBoost	胫围	胫长	胸宽	骨盆宽	腰围
136	DT	体重	胸深	骨盆宽	胸宽	体斜长
	RF	体重	胸宽	骨盆宽	胸深	腰围
	GBDT	体重	骨盆宽	胸宽	胸深	体斜长
	XGBoost	体重	骨盆宽	胫围	胸宽	腰围

日龄/d Age	模型 Model	二分类 Binary classification		三分类 Tertiary classification
日龄/d Age	模型 Model	准确率/% ACC	时间/s Time	准确率/% ACC	时间/s Time
58	KNN	79.41	0.30	66.81	0.25
	MultinomialNB	57.56	0.05	63.03	0.04
	SVM	51.68	0.47	63.03	1.21
	Bagging	100.00	2.10	100.00	1.90
72	KNN	59.24	0.28	62.61	0.30
	MultinomialNB	51.68	0.05	63.03	0.05
	SVM	51.68	0.44	63.03	0.75
	Bagging	94.54	1.87	100.00	2.14
86	KNN	68.07	0.63	65.97	0.26
	MultinomialNB	52.10	0.06	63.03	0.05
	SVM	56.30	0.46	63.03	0.73
	Bagging	100.00	1.97	100.00	2.02
104	KNN	65.55	0.39	63.87	0.27
	MultinomialNB	51.68	0.07	63.03	0.05
	SVM	56.30	0.58	63.03	0.69
	Bagging	99.58	2.27	100.00	1.88
119	KNN	65.97	0.41	63.45	0.29
	MultinomialNB	51.68	0.06	63.03	0.05
	SVM	51.68	0.50	63.03	0.76
	Bagging	99.58	0.58	99.58	2.28
136	KNN	68.07	0.29	75.21	0.32
	MultinomialNB	59.24	0.05	63.45	0.06
	SVM	59.66	0.53	63.03	0.70
	Bagging	99.58	1.91	100.00	2.50

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