A multi-sensor approach to calving detection

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY Information Processing in Agriculture Pub Date : 2024-03-01 DOI:10.1016/j.inpa.2022.07.002

Anita Z. Chang, David L. Swain, Mark G. Trotter

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Abstract

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.

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一种多传感器产犊检测方法

远程牲畜监控系统的出现为提高农场生产率、效率和福利提供了多种可能性。这些系统的一个潜在应用是检测产犊事件。本研究介绍了利用支持向量机方法整合多种传感器来源的数据，包括加速度计、全球导航卫星系统（GNSS）、加速度计衍生的反刍算法、步行过称装置和气象站，用于产仔检测。性能最好的模型利用了来自全球导航卫星系统、反刍算法和气象站的数据，准确率达到 98.6%，灵敏度为 88.5%，特异性为 100%。该模型排名靠前的特征主要来自全球导航卫星系统。本研究概述了如何在农场整合各种传感器系统，以最大限度地提高产犊检测能力，从而改善生产和福利状况。

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来源期刊

Information Processing in Agriculture Agricultural and Biological Sciences-Animal Science and Zoology

CiteScore

21.10

自引率

0.00%

发文量

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