劳凤丹,杜晓冬,滕光辉.基于深度图像的蛋鸡行为识别方法[J].农业机械学报,2017,48(1):155-162.
LAO Fengdan,DU Xiaodong,TENG Guanghui.Automatic Recognition Method of Laying Hen Behaviors Based on Depth Image Processing[J].Transactions of the Chinese Society for Agricultural Machinery,2017,48(1):155-162.
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基于深度图像的蛋鸡行为识别方法   [下载全文]
Automatic Recognition Method of Laying Hen Behaviors Based on Depth Image Processing   [Download Pdf][in English]
投稿时间:2016-08-15  
DOI:10.6041/j.issn.1000-1298.2017.01.020
中文关键词:  深度图像  蛋鸡  行为  自动识别
基金项目:“十二五”国家科技支撑计划项目(2014BAD08B00—01)
作者单位
劳凤丹 中国农业大学 
杜晓冬 中国农业大学 
滕光辉 中国农业大学 
中文摘要:基于深度图像分析技术研究了一种针对蛋鸡群体行为(分布指数、水平活跃度和垂直活跃度)和群体中个体行为(采食、躺、站和坐)经济简单的自动识别方法。系统由1台3D照相机同步采集数字和深度图像数据,并开发软件进行蛋鸡行为的自动识别,系统5s采集1次图像数据,共进行10d的数据采集。描述了行为识别算法并进行了行为识别结果分析。算法对蛋鸡的采食、躺、站和坐的识别准确率分别为90.3%、91.5%、87.5%和56.2%。坐行为识别率较低的原因主要是有时蛋鸡站着探索地面会被误判为坐,这可能与两者之间的分割阈值不够精确有关。
LAO Fengdan  DU Xiaodong  TENG Guanghui
China Agricultural University,China Agricultural University and China Agricultural University
Key Words:depth image  laying hens  behaviors  automatic recognition
Abstract:Animal behaviors are reflective of its welfare state. They contain important information that can enable producers to better manage livestock. Yet it is more difficult in recognizing the behaviors of group laying hens than other big size animals. Large numbers of hens, homogeneous in appearance, high stocking density and variable body size all contribute to this situation. A computer vision-based system was developed which can automatically recognize group behaviors (distribution index, horizontal activity index and vertical activity index) and individual behaviors (feeding, lying, standing and sitting) of group hens. The system consisted of a 3D camera that simultaneously acquired digital and depth images and a software program that detected and identified the behaviors. The computational algorithm for the analysis of depth images was presented and its performance in recognizing the behaviors as compared with manual recognition was analyzed. The images were acquired at 5s intervals in 10d period. The algorithm had the following accuracy of individual behavioral classification: 90.3% in feeding, 91.5% in lying, 87.5% in standing and 56.2% in sitting. The lower classification accuracy for the sitting presumably stemmed to imprecise segmentation valve value between sitting and standing and sometimes mistook hen’s standing behavior (exploring in ground) for sitting which could be improved in later test. Hence the reported system provided an effective way to automatically process and classify hen’s group and individual behaviors. This tool was conducive to investigate behavioral responses and time budget of laying hens and facility design and management practice.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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