Classification of broiler behaviours using triaxial accelerometer and machine learning

Yang, Xiao; Zhao, Yang; Street, Garrett M.; Huang, Yongliang; To, S. D. Filip; Purswell, J.L.

doi:10.1016/j.animal.2021.100269

Cited by 42 publications

(26 citation statements)

References 45 publications

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“…At this time, it is not clear how the prototype can be applied to large commercial flocks. However, PLF is a rapidly developing field and new developments have to be followed (Yang et al., 2021 ).…”

Section: Assessmentmentioning

confidence: 99%

Welfare of broilers on farm

Nielsen¹,

Álvarez²,

Bicout³

et al. 2023

EFS2

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This Scientific Opinion considers the welfare of domestic fowl (Gallus gallus) related to the production of meat (broilers) and includes the keeping of day-old chicks, broiler breeders, and broiler chickens. Currently used husbandry systems in the EU are described. Overall, 19 highly relevant welfare consequences (WCs) were identified based on severity, duration and frequency of occurrence: 'bone lesions', 'cold stress', 'gastro-enteric disorders', 'group stress', 'handling stress', 'heat stress', 'isolation stress', 'inability to perform comfort behaviour', 'inability to perform exploratory or foraging behaviour', 'inability to avoid unwanted sexual behaviour', 'locomotory disorders', 'prolonged hunger', 'prolonged thirst', 'predation stress', 'restriction of movement', 'resting problems', 'sensory under-and overstimulation', 'soft tissue and integument damage' and 'umbilical disorders'. These WCs and their animal-based measures (ABMs) that can identify them are described in detail. A variety of hazards related to the different husbandry systems were identified as well as ABMs for assessing the different WCs. Measures to prevent or correct the hazards and/or mitigate each of the WCs are listed. Recommendations are provided on quantitative or qualitative criteria to answer specific questions on the welfare of broilers and related to genetic selection, temperature, feed and water restriction, use of cages, light, air quality and mutilations in breeders such as beak trimming, de-toeing and comb dubbing. In addition, minimal requirements (e.g. stocking density, group size, nests, provision of litter, perches and platforms, drinkers and feeders, of covered veranda and outdoor range) for an enclosure for keeping broiler chickens (fast-growing, slower-growing and broiler breeders) are recommended. Finally, 'total mortality', 'wounds', 'carcass condemnation' and 'footpad dermatitis' are proposed as indicators for monitoring at slaughter the welfare of broilers on-farm.

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Section: Assessmentmentioning

confidence: 99%

Welfare of broilers on farm

Nielsen¹,

Álvarez²,

Bicout³

et al. 2023

EFS2

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“…Analyzing animal behavior via tracking and monitoring has been implemented by different tools including radio-frequency identification (RFID) transponders [ 17 ], accelerometers [ 18 ], and cameras coupled with image analysis [ 19 ]. Videos or images have been analyzed and used for studies on broilers including bodyweight [ 20 , 21 ], health status [ 22 , 23 ], behavior [ 24 ], flock movement [ 25 ], and locomotion/activity [ 3 , 26 ].…”

Section: Related Workmentioning

confidence: 99%

Keypoint Detection for Injury Identification during Turkey Husbandry Using Neural Networks

Volkmann

Zelenka

Devaraju

et al. 2022

Sensors

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Injurious pecking against conspecifics is a serious problem in turkey husbandry. Bloody injuries act as a trigger mechanism to induce further pecking, and timely detection and intervention can prevent massive animal welfare impairments and costly losses. Thus, the overarching aim is to develop a camera-based system to monitor the flock and detect injuries using neural networks. In a preliminary study, images of turkeys were annotated by labelling potential injuries. These were used to train a network for injury detection. Here, we applied a keypoint detection model to provide more information on animal position and indicate injury location. Therefore, seven turkey keypoints were defined, and 244 images (showing 7660 birds) were manually annotated. Two state-of-the-art approaches for pose estimation were adjusted, and their results were compared. Subsequently, a better keypoint detection model (HRNet-W48) was combined with the segmentation model for injury detection. For example, individual injuries were classified using “near tail” or “near head” labels. Summarizing, the keypoint detection showed good results and could clearly differentiate between individual animals even in crowded situations.

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“…Several sensors for recording individual broiler behavior, both wearable and remote, have been introduced over the years. Wearable sensors include, for example, accelerometers ( Yang et al, 2021 ), inertial measurement units ( IMUs ; Derakhshani et al, 2022 ), radio frequency identification ( RFID ; van der Sluis et al, 2020 ) and ultra-wideband tracking ( UWB ; van der Sluis et al, 2019 ). Wearable sensors could be considered impractical and expensive, as all animals need a sensor for individual phenotypes, and some sensors need batteries (accelerometers, IMU, UWB) or an extensive network of antennas (RFID).…”

Section: Introductionmentioning

confidence: 99%