| 基于机器视觉的育肥猪分群系统设计与试验 |
| |
| 引用本文： | 张建龙,庄晏榕,周康,滕光辉.基于机器视觉的育肥猪分群系统设计与试验[J].农业工程学报,2020,36(17):174-181. |
| |
| 作者姓名： | 张建龙 庄晏榕 周康 滕光辉 |
| |
| 作者单位： | 中国农业大学水利与土木工程学院,北京 100083;中国农业大学农业农村部设施农业工程重点实验室,北京 100083 |
| |
| 基金项目： | 十三五国家重点研发计划（2016YFD0700204） |
| |
| 摘 要： | 为控制育肥猪出栏时的体质量差异,该研究开发了一套基于机器视觉技术的育肥猪分群系统,该系统通过机器视觉技术和卷积神经网络模型代替传统地磅对猪只体质量进行估测,可有效避免粪污对设备精度的影响及腐蚀;以前一天全部猪只体质量数据从小到大排列的第30%个数据作为当日的分群基准质量,将大于等于基准质量的视为长势较快的猪只,小于基准质量的视为长势较慢的猪只,每次采食按照猪只长势快慢分为2群进行饲喂;该系统依托于LabVIEW软件开发平台和物联网系统构建,平均每头猪只通过系统时间为6.2 s。为验证该系统的实际应用效果开展了为期30 d的现场试验,将饲喂于装有分群系统猪栏中的120头长白育肥猪作为试验组,由分群系统按猪只长势快慢分群饲喂;将饲喂于传统猪栏中的120头长白育肥猪作为对照组,按照传统人工调栏的方式进行饲喂。试验开始时试验组和对照组猪只平均体质量分别为32.21、31.76 kg,标准差分为别2.61和2.49 kg;结束时试验组和对照组猪只平均体质量分别为57.68、57.41 kg,标准差分为别5.26和5.51 kg,总料肉比分别为2.31和2.34,期间试验组猪只体质量的标准差小于对照组,但是2组猪只平均体质量、标准差、总料肉比均不存在显著差异,表明采用该系统对猪只进行分群饲喂控制猪只体质量差异效果等同于人工调栏,同时可以节省人力成本,缓解农业劳动力短缺的压力。该研究也可为母猪饲喂站、种猪测定站等智能化养猪设备的研发提供参考。
|
| 关 键 词： | 机器视觉 动物 育肥猪 LabVIEW 分群系统 |
| 收稿时间： | 2020/5/18 0:00:00 |
| 修稿时间： | 2020/6/28 0:00:00 |
Design of automatic group sorting system for fattening pigs based on machine vision |
| |
| Zhang Jianlong,Zhuang Yanrong,Zhou Kang,Teng Guanghui.Design of automatic group sorting system for fattening pigs based on machine vision[J].Transactions of the Chinese Society of Agricultural Engineering,2020,36(17):174-181. |
| |
| Authors: | Zhang Jianlong Zhuang Yanrong Zhou Kang Teng Guanghui |
| |
| Institution: | 1. College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China; 2. Key Laboratory of Agricultural Engineering in Structure and Environment, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100083, China |
| |
| Abstract: | Excessive weight variation among slaughtered fattening pigs has posed a practical challenge on the economic benefits of pig farm in recent years. Therefore, live weight homogeneity of pig batches during fattening has drawn great interest in the pig industry. In this study, an automatic sorting system was developed for the growing and fattening pigs, using the machine vision technology and Convolutional Neural Network (CNN) framework, in order to reduce the weight variation among pigs, and further to save labor in the subsequent process. A CNN model in the system was used to estimate the weight of pigs, instead of ground scale. This arrangement can effectively avoid the influence of manure on the surface corrosion and the accuracy of facilities. The back images (200×100 pixels) of pigs served as the input data in the model, thereby to estimate the weight of pigs ranging from 25 to 102 kg with the accuracy of 93%, and the average estimated time of 0.16 s. In view of changing every day, the standard value of sorting was set as the 30th percentile of pigs weight from the previous day in an ascending order. The pigs that heavier than the baseline were considered as the fast-growing pigs (FP), otherwise, they were supposed as the slow-growing pigs (SP). The modelling system was performed on the LabVIEW software development platform and internet of things, where the average time for each pig to pass through the system was 6.2s. Field experiments were carried out to verify the application effect of the system at a commercial pig farm in Shandong province in March, 2019. The experimental pig house was divided into 12 pens, four of which were merged and installed with the sorting system. The experimental pen (EP) consisted of the feeding area for FP, feeding area for SP, and lying area. The pigs fed in EP were treated as the experimental group. Specifically, the pigs first passed through the sorting system before feeding, and then entered the corresponding feeding area after being marked as SP or FP. Therefore, two groups each time, including SP and FP, were categorized after the pigs were fed. The pigs in other four unmodified pens were regarded as the control group, in which the pigs were fed and sorted by traditionally manual method. At the beginning of the experiment, the initial average weights of the pigs in the experimental and control group were 32.21 kg and 31.76 kg, with the values of standard deviation (SD) of 2.61 and 2.49, respectively. At the end of experiment, the average weights of the pigs in the experimental and control group were 57.68 kg and 57.41 kg, where the values of SD were 5.26 and 5.51, and the total feed-to-meat ratios were 2.31 and 2.34, respectively. The number of pigs in the weight range of 45~50kg in the experimental group was less than that of control group. There was no significant difference in the average weight, SD, and total feed-to-meat ratio between the two groups during the experiment. In the early stage of the experiment, the weight variance of the experimental group increased faster than that of the control group, for the reason that the grouping system was not activated, and then the change was slower than that of the control group. The results indicated that the proposed system can be equivalent to the manual adjustment for the group feeding of pigs, while, the sorting system can be used for group feeding to save labor. The findings can also provide a sound theoretical reference for the development of intelligent pig feeding equipment, such as sow feeding and breeding station in the pig industry. |
| |
| Keywords: | machine vision animals growing-finishing pigs LabVIEW sorting system |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
| 点击此处可从《农业工程学报》浏览原始摘要信息 |
| 点击此处可从《农业工程学报》下载免费的PDF全文 |
|
