A method of monitoring and locating eggs laid by breeding geese based on photoelectric sensing technology

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY
Yidan Xu , Qiuju Xie , Liwei Wang
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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.

一种基于光电传感技术的种鹅产蛋监测与定位方法
在目前的种鹅养殖场,个体产蛋的检测主要依靠养殖场工人的一些判断经验。目前,已经开发了一些带有图像和称重传感器的产蛋检测系统,这些系统只能发出产蛋的信号,而不能确定蛋的位置。同时,由于鸡舍光线暗淡等环境限制,系统的检出率不高。因此,为了解决上述问题,提出了一种将射频(RF)技术与光电传感器技术相结合,结合几何计算原理的智能检测定位系统。为了提高种蛋生产领域的自动检测和定位水平,本研究对非笼养状态下种鹅的个体产蛋信息进行了检测。结果表明,在距离蛋窝底板3.5 cm、光敏电阻模块间距为5 cm、外部光照强度小于110 LUX的条件下,可以实现对人造草坪填充的蛋窝中的蛋的精确检测和定位。结果表明,在合适的稻草水平下,雁蛋位置识别误差为0.443 cm。因此,本研究开发的监测系统和定位方法可为个体产蛋行为分析提供参考,并可提高种鹅生产的自动采蛋能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Information Processing in Agriculture
Information Processing in Agriculture Agricultural and Biological Sciences-Animal Science and Zoology
CiteScore
21.10
自引率
0.00%
发文量
80
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
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