{"title":"Pig Weight Estimation According to RGB Image Analysis","authors":"Andras Kárpinszky, Gergely Dobsinszki","doi":"10.18690/agricsci.20.1.6","DOIUrl":null,"url":null,"abstract":"In pig farming, knowing the exact weight of each animal is critical for the owner. Such information can help determine the amount and type of feed that needs to be fed to a specific fattening pig. Weighing pigs has always been problematic, because it is highly time consuming, and herding the pigs on the scale is extremely cumbersome. Moreover, it causes stress to the animals. The aim of our study was to build an RGB-based system that could estimate the daily weight of pigs and individual animal weight. The study was set up in a 100-day rotation in a commercial pig farm where we monitored 32 pigs. We developed a system to identify the features of the pigs, more particularly the head, shoulder, belly, and rump part. Three different modelswere tested, and their main differences were linked to image processing and training data. Using these models, we received higher than 97% accuracy between the predicted and the manually recorded weight of the animals. This system allows owners to manage and monitor their pigs using our web interface, allowing them to make crucial decisions during the farming process.","PeriodicalId":37655,"journal":{"name":"中国农业科学","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"中国农业科学","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18690/agricsci.20.1.6","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In pig farming, knowing the exact weight of each animal is critical for the owner. Such information can help determine the amount and type of feed that needs to be fed to a specific fattening pig. Weighing pigs has always been problematic, because it is highly time consuming, and herding the pigs on the scale is extremely cumbersome. Moreover, it causes stress to the animals. The aim of our study was to build an RGB-based system that could estimate the daily weight of pigs and individual animal weight. The study was set up in a 100-day rotation in a commercial pig farm where we monitored 32 pigs. We developed a system to identify the features of the pigs, more particularly the head, shoulder, belly, and rump part. Three different modelswere tested, and their main differences were linked to image processing and training data. Using these models, we received higher than 97% accuracy between the predicted and the manually recorded weight of the animals. This system allows owners to manage and monitor their pigs using our web interface, allowing them to make crucial decisions during the farming process.
中国农业科学Agricultural and Biological Sciences-Agricultural and Biological Sciences (all)
CiteScore
1.90
自引率
0.00%
发文量
17516
期刊介绍:
Chinese Agricultural Science is a comprehensive, academic journal co-sponsored by the Chinese Academy of Agricultural Sciences and the Chinese Society of Agriculture. Founded in 1960, the journal is published publicly in the form of a bimonthly magazine. The contents of the journal include crop genetic breeding, cultivation, plant protection, soil fertiliser, horticulture, food science and engineering, animal husbandry and veterinary medicine, etc. It aims to promote the sustainable development of high-yield, high-quality, high-efficiency and environmentally friendly agriculture and animal husbandry.
The aim of the journal is to report on the scientific research results of agriculture and animal husbandry in China, to enhance the innovation capacity of agricultural science and technology, to promote academic exchanges at home and abroad, and to serve the development of modern agricultural science and technology and scientific and technological progress. In addition, Chinese Agricultural Science is included in several international retrieval systems, including American Chemical Abstracts CA, Scopus, GeoBase, Russian Journal of Abstracts, CABI (Centre for Agricultural and Biological Information International) of the United Kingdom, and AGRIS (Food and Agriculture Organization of the United Nations).