{"title":"一种多传感器产犊检测方法","authors":"Anita Z. Chang, David L. Swain, Mark G. Trotter","doi":"10.1016/j.inpa.2022.07.002","DOIUrl":null,"url":null,"abstract":"<div><p>The advent of remote livestock monitoring systems provides numerous possibilities for improving on-farm productivity, efficiency, and welfare. One potential application for these systems is for the detection of calving events. This study describes the integration of data from multiple sensor sources, including accelerometers, global navigation satellite systems (GNSS), an accelerometer-derived rumination algorithm, a walk-over-weigh unit, and a weather station for parturition detection using a support vector machine approach. The best performing model utilised data from GNSS, the ruminating algorithm, and weather stations to achieve 98.6% accuracy, with 88.5% sensitivity and 100% specificity. The top-ranking features of this model were primarily GNSS derived. This study provides an overview as to how various sensor systems could be integrated on-farm to maximise calving detection for improved production and welfare outcomes.</p></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 1","pages":"Pages 45-64"},"PeriodicalIF":7.7000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214317322000671/pdfft?md5=b8ef2a2b45bdd8fe11c661dd11b2b2c7&pid=1-s2.0-S2214317322000671-main.pdf","citationCount":"0","resultStr":"{\"title\":\"A multi-sensor approach to calving detection\",\"authors\":\"Anita Z. Chang, David L. Swain, Mark G. Trotter\",\"doi\":\"10.1016/j.inpa.2022.07.002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The advent of remote livestock monitoring systems provides numerous possibilities for improving on-farm productivity, efficiency, and welfare. One potential application for these systems is for the detection of calving events. This study describes the integration of data from multiple sensor sources, including accelerometers, global navigation satellite systems (GNSS), an accelerometer-derived rumination algorithm, a walk-over-weigh unit, and a weather station for parturition detection using a support vector machine approach. The best performing model utilised data from GNSS, the ruminating algorithm, and weather stations to achieve 98.6% accuracy, with 88.5% sensitivity and 100% specificity. The top-ranking features of this model were primarily GNSS derived. This study provides an overview as to how various sensor systems could be integrated on-farm to maximise calving detection for improved production and welfare outcomes.</p></div>\",\"PeriodicalId\":53443,\"journal\":{\"name\":\"Information Processing in Agriculture\",\"volume\":\"11 1\",\"pages\":\"Pages 45-64\"},\"PeriodicalIF\":7.7000,\"publicationDate\":\"2024-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2214317322000671/pdfft?md5=b8ef2a2b45bdd8fe11c661dd11b2b2c7&pid=1-s2.0-S2214317322000671-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information Processing in Agriculture\",\"FirstCategoryId\":\"1091\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2214317322000671\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRICULTURE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Processing in Agriculture","FirstCategoryId":"1091","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214317322000671","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
The advent of remote livestock monitoring systems provides numerous possibilities for improving on-farm productivity, efficiency, and welfare. One potential application for these systems is for the detection of calving events. This study describes the integration of data from multiple sensor sources, including accelerometers, global navigation satellite systems (GNSS), an accelerometer-derived rumination algorithm, a walk-over-weigh unit, and a weather station for parturition detection using a support vector machine approach. The best performing model utilised data from GNSS, the ruminating algorithm, and weather stations to achieve 98.6% accuracy, with 88.5% sensitivity and 100% specificity. The top-ranking features of this model were primarily GNSS derived. This study provides an overview as to how various sensor systems could be integrated on-farm to maximise calving detection for improved production and welfare outcomes.
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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