AI in Sustainable Pig Farming: IoT Insights into Stress and Gait

IF 3.3 2区 农林科学 Q1 AGRONOMY
S. Neethirajan
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引用次数: 1

Abstract

This paper pioneers a novel exploration of environmental impacts in livestock farming, focusing on pig farming’s intersection with climate change and sustainability. It emphasizes the transformative potential of data-driven Artificial Intelligence (AI) methodologies, specifically the Internet of Things (IoT) and multimodal data analysis, in promoting equitable and sustainable food systems. The study observes five pigs aged 86 to 108 days using a tripartite sensor that records heart rate, respiration rate, and accelerometer data. The unique experimental design alternates between periods of isolation during feeding and subsequent pairing, enabling the investigation of stress-induced changes. Key inquiries include discerning patterns in heart rate data during isolation versus paired settings, fluctuations in respiration rates, and behavioral shifts induced by isolation or pairing. The study also explores the potential detection of gait abnormalities, correlations between pigs’ age and their gait or activity patterns, and the evolution of pigs’ walking abilities with age. The paper scrutinizes accelerometer data to detect activity changes when pigs are paired, potentially indicating increased stress or aggression. It also examines the adaptation of pigs to alternating isolation and pairing over time and how their heart rate, respiration rate, and activity data reflect this process. The study considers other significant variables, such as time of day and isolation duration, affecting the pigs’ physiological parameters. Sensor data are further utilized to identify behavioral patterns during periods of feeding, isolation, or pairing. In conclusion, this study harnesses IoT and multimodal data analysis in a groundbreaking approach to pig welfare research. It underscores the compelling potential of technology to inform about overall pig welfare, particularly stress levels and gait quality, and the power of data-driven insights in fostering equitable, healthy, and environmentally conscious livestock production systems.
可持续养猪业中的人工智能:压力和步态的物联网洞察
本文对畜牧业的环境影响进行了新颖的探索,重点关注养猪业与气候变化和可持续性的交叉。它强调了数据驱动的人工智能(AI)方法,特别是物联网(IoT)和多模式数据分析在促进公平和可持续粮食系统方面的变革潜力。该研究观察了5只年龄在86至108天之间的猪,使用了一个记录心率、呼吸频率和加速度计数据的三联传感器。独特的实验设计在饲养期间的隔离和随后的配对之间交替进行,使研究压力引起的变化成为可能。关键问题包括隔离与配对设置期间心率数据的识别模式、呼吸频率的波动以及隔离或配对引起的行为变化。该研究还探讨了步态异常的潜在检测,猪的年龄与其步态或活动模式之间的相关性,以及猪的行走能力随年龄的演变。这篇论文仔细研究了加速度计数据,以检测猪在配对时的活动变化,这可能表明压力或攻击性增加。它还研究了猪对交替隔离和配对的适应情况,以及它们的心率、呼吸率和活动数据如何反映这一过程。该研究考虑了影响猪生理参数的其他重要变量,如一天中的时间和隔离时间。传感器数据进一步用于识别进食、隔离或配对期间的行为模式。总之,本研究将物联网和多模态数据分析作为猪福利研究的一种开创性方法。它强调了技术在了解猪的整体福利,特别是压力水平和步态质量方面的巨大潜力,以及数据驱动的洞察力在促进公平、健康和环保的畜牧生产系统方面的力量。
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
Agriculture-Basel
Agriculture-Basel Agricultural and Biological Sciences-Food Science
CiteScore
4.90
自引率
13.90%
发文量
1793
审稿时长
11 weeks
期刊介绍: Agriculture (ISSN 2077-0472) is an international and cross-disciplinary scholarly and scientific open access journal on the science of cultivating the soil, growing, harvesting crops, and raising livestock. We will aim to look at production, processing, marketing and use of foods, fibers, plants and animals. The journal Agriculturewill publish reviews, regular research papers, communications and short notes, and there is no restriction on the length of the papers. Our aim is to encourage scientists to publish their experimental and theoretical research in as much detail as possible. Full experimental and/or methodical details must be provided for research articles.
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