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基于深度图像和BP神经网络的肉鸡体质量估测模型
引用本文:王琳,孙传恒,李文勇,吉增涛,张翔,王以忠,雷鹏,杨信廷.基于深度图像和BP神经网络的肉鸡体质量估测模型[J].农业工程学报,2017,33(13):199-205.
作者姓名:王琳  孙传恒  李文勇  吉增涛  张翔  王以忠  雷鹏  杨信廷
作者单位:1. 国家农业信息化工程技术研究中心/农业部农业信息技术重点实验室/北京市农业物联网工程技术研究中心,北京 100097;天津科技大学电子信息与自动化学院,天津 300222;2. 国家农业信息化工程技术研究中心/农业部农业信息技术重点实验室/北京市农业物联网工程技术研究中心,北京,100097;3. 天津科技大学电子信息与自动化学院,天津,300222
基金项目:国家自然科学基金青年基金项目(61601034);国家重点研发计划课题(2016YFD0700202)
摘    要:针对现阶段肉鸡称重复杂、福利降低问题,提出了一种基于深度图像的肉鸡体质量估测模型建立方法。该方法首先对深度图像进行图像预处理,再利用数值积分法提取出目标特征,并结合BP神经网络,实现群体肉鸡的体质量估测。估测结果与实际测量结果进行对比,研究结果表明两者的均方根误差为0.048,平均相对误差为3.3%,绝对误差在0.001 0~0.068 2 kg范围内,最优拟合度为0.994 3,具有较好的推广应用价值。该方法较为准确的估测出肉鸡体质量,并为用机器视觉的方法估测肉鸡生长发育规律提供了新的思路。

关 键 词:图像处理  模型  动物  深度图像  特征提取  肉鸡  体质量估测
收稿时间:2017/1/13 0:00:00
修稿时间:2017/5/25 0:00:00

Establishment of broiler quality estimation model based on depth image and BP neural network
Wang Lin,Sun Chuanheng,Li Wenyong,Ji Zengtao,Zhang Xiang,Wang Yizhong,Lei Peng and Yang Xinting.Establishment of broiler quality estimation model based on depth image and BP neural network[J].Transactions of the Chinese Society of Agricultural Engineering,2017,33(13):199-205.
Authors:Wang Lin  Sun Chuanheng  Li Wenyong  Ji Zengtao  Zhang Xiang  Wang Yizhong  Lei Peng and Yang Xinting
Affiliation:1. National Agricultural Information Engineering Research Center of Ministry of Agriculture/Agricultural Information Technology Key Laboratory of Beijing agricultural IOT Engineering Technology Research Center, Beijing 100097, China; 2. Tianjin University of Science and Technology School of Electronic Information and Automation, Tianjin 300222, China;,1. National Agricultural Information Engineering Research Center of Ministry of Agriculture/Agricultural Information Technology Key Laboratory of Beijing agricultural IOT Engineering Technology Research Center, Beijing 100097, China;,1. National Agricultural Information Engineering Research Center of Ministry of Agriculture/Agricultural Information Technology Key Laboratory of Beijing agricultural IOT Engineering Technology Research Center, Beijing 100097, China;,1. National Agricultural Information Engineering Research Center of Ministry of Agriculture/Agricultural Information Technology Key Laboratory of Beijing agricultural IOT Engineering Technology Research Center, Beijing 100097, China;,1. National Agricultural Information Engineering Research Center of Ministry of Agriculture/Agricultural Information Technology Key Laboratory of Beijing agricultural IOT Engineering Technology Research Center, Beijing 100097, China; 2. Tianjin University of Science and Technology School of Electronic Information and Automation, Tianjin 300222, China;,2. Tianjin University of Science and Technology School of Electronic Information and Automation, Tianjin 300222, China;,2. Tianjin University of Science and Technology School of Electronic Information and Automation, Tianjin 300222, China; and 1. National Agricultural Information Engineering Research Center of Ministry of Agriculture/Agricultural Information Technology Key Laboratory of Beijing agricultural IOT Engineering Technology Research Center, Beijing 100097, China;
Abstract:Abstract: Body weight is one of the main growth indices in broiler production, which is a comprehensive parameter in the broiler growth. The most common method to measure weight is manual operation, in which the broiler is captured and placed on the electronic scale. This method decreases animal''s welfare and increases labor; in addition, it also will affect the yield and quality, and even cause the death of broilers. It can''t be applied in commercial farms. The Kinect 3D (three-dimensional) camera which can measure the phenotype features with a non-invasive way has been applied into animal''s weight acquisition. A broiler quality estimation method based on depth image was proposed in this paper. Yuncheng partridge shank chickens were chosen as research objects and an image collection system was constructed in a local farm. In this experiment, 150 broilers were selected randomly and the duration was the lifespan, 30 days. The acquisition system is composed of a Kinect depth camera, an industrial control computer, a serial port switching electronic scale and a fence. The procedure of image preprocess consists of image cropping, median filtering, OTSU threshold segmentation and binarization. And the maximum target in the binary image after morphological reconstruction, such as opening and closing, was recognized as object. In the feature extraction stage, 9 features were extracted using a mathematical geometry method, including area, eccentricity, width, length, radius, perimeter, volume, back width, and day age. In the model construction stage, a BP (back propagation) neural network was designed with 9 feature inputs and 1 weight output. After sampling randomly, 1985 samples were used as the training set, and the remaining 20% were used as the test set. Based on these data, the body mass estimation model was established to realize the population mass estimation. Compared with the measured results, the estimation has good performance. The root mean square error (RMSE) is 0.048 kg, the mean relative error (MRE) is 3.3%, the optimal fitness is about 0.994 3, the minimum relative error is 0.5% and the maximum relative error is about 11%. Different feature group and BP neural network were designed and trained. From the results of different modeling, it can be seen that the influence of 3D feature on the body mass is smaller than that of 2D feature. For the 3D features, the target volume has the least impact on the results, and for the feature from 2D group, the projection area has the greatest impact on the results. The fitting results of model which used 9 input parameters were the best. These results show that the proposed method is feasible and effective for constructing broiler quality estimation model. It provides theoretical basis for estimating broiler growth with machine vision technology as well as precision feeding.
Keywords:image processing  models  animals  depth image  feature extraction  broiler  body mass estimation
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