{"title":"A method of monitoring and locating eggs laid by breeding geese based on photoelectric sensing technology","authors":"Yidan Xu , Qiuju Xie , Liwei Wang","doi":"10.1016/j.inpa.2021.06.002","DOIUrl":null,"url":null,"abstract":"<div><p>On the current breeding goose farm, the detection of individual egg laying mainly depends on some judgement experiences of farm workers. At present, there have been some egg laying detection systems developed with images and weighing sensors, which only signal the eggs being laid, but no egg position being achieved. Meanwhile, the detection rate of the system is not high due to environment limitations like dim light of the goose barn. Therefore, to solve these problems mentioned above, an intelligent detection and positioning system is proposed by integrating technologies of the Radio Frequency (RF) and photoelectric sensors, together with the geometric calculation principle. In this research, individual egg laying information of breeding geese in a non-cage state was examined to improve the level of automatic detection and positioning in the field of breeder egg production. The results showed that an accurate detection and positioning of an egg in a nest filled with the artificial turf could be achieved under some conditions: the height of sensor is 3.5 cm from the bottom plate of the egg laying nest, the spacing of the photoresistor module is 5 cm, and the external light intensity is less than 110 LUX. It also shown that the error of the goose egg position recognition is 0.443 cm with a suitable level of straw in the nest. Therefore, the monitoring system and positioning method that was developed in this research could provide a reference for the analysis of individual egg laying behavior, and could result in an improvement in the automatic egg collection for the breeding geese production.</p></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"9 3","pages":"Pages 406-416"},"PeriodicalIF":7.7000,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.inpa.2021.06.002","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Processing in Agriculture","FirstCategoryId":"1091","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214317321000494","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
On the current breeding goose farm, the detection of individual egg laying mainly depends on some judgement experiences of farm workers. At present, there have been some egg laying detection systems developed with images and weighing sensors, which only signal the eggs being laid, but no egg position being achieved. Meanwhile, the detection rate of the system is not high due to environment limitations like dim light of the goose barn. Therefore, to solve these problems mentioned above, an intelligent detection and positioning system is proposed by integrating technologies of the Radio Frequency (RF) and photoelectric sensors, together with the geometric calculation principle. In this research, individual egg laying information of breeding geese in a non-cage state was examined to improve the level of automatic detection and positioning in the field of breeder egg production. The results showed that an accurate detection and positioning of an egg in a nest filled with the artificial turf could be achieved under some conditions: the height of sensor is 3.5 cm from the bottom plate of the egg laying nest, the spacing of the photoresistor module is 5 cm, and the external light intensity is less than 110 LUX. It also shown that the error of the goose egg position recognition is 0.443 cm with a suitable level of straw in the nest. Therefore, the monitoring system and positioning method that was developed in this research could provide a reference for the analysis of individual egg laying behavior, and could result in an improvement in the automatic egg collection for the breeding geese production.
期刊介绍:
Information Processing in Agriculture (IPA) was established in 2013 and it encourages the development towards a science and technology of information processing in agriculture, through the following aims: • Promote the use of knowledge and methods from the information processing technologies in the agriculture; • Illustrate the experiences and publications of the institutes, universities and government, and also the profitable technologies on agriculture; • Provide opportunities and platform for exchanging knowledge, strategies and experiences among the researchers in information processing worldwide; • Promote and encourage interactions among agriculture Scientists, Meteorologists, Biologists (Pathologists/Entomologists) with IT Professionals and other stakeholders to develop and implement methods, techniques, tools, and issues related to information processing technology in agriculture; • Create and promote expert groups for development of agro-meteorological databases, crop and livestock modelling and applications for development of crop performance based decision support system. Topics of interest include, but are not limited to: • Smart Sensor and Wireless Sensor Network • Remote Sensing • Simulation, Optimization, Modeling and Automatic Control • Decision Support Systems, Intelligent Systems and Artificial Intelligence • Computer Vision and Image Processing • Inspection and Traceability for Food Quality • Precision Agriculture and Intelligent Instrument • The Internet of Things and Cloud Computing • Big Data and Data Mining