A Review on Computer Vision Technology for Monitoring Poultry Farm—Application, Hardware, and Software

Aziz, Nur Syazarin Natasha Abd; Daud, Salwani Mohd; Dziyauddin, Rudzidatul Akmam; Adam, Mohamad Zulkefli; Azizan, Azızul

doi:10.1109/access.2020.3047818

Cited by 20 publications

(8 citation statements)

References 92 publications

(219 reference statements)

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“…Generally, image acquisition using computer vision technology in poultry housings is realized either by top-view or side-view camera position [ 52 ]. Previously, different authors used top-view camera imaging to measure broilers’ weight [ 20 , 21 ], to analyze the distribution of the animals in the barn [ 53 ], or to detect sick birds [ 22 ].…”

Section: Discussion and Further 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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Section: Discussion and Further 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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“…Computer vision in poultry farming involves two components: hardware and software. Hardware includes imagers and light sources, whereas software includes data acquisition, processing, and analysis Figure summarizes different hardware and software tools used in computer vision for health and welfare of birds.…”

Section: Role Of Emerging Technologies In Poultry Health Managementmentioning

confidence: 99%

“…In the literature, , morphological features such as age, area, perimeter, and volume of the poultry have been used for broiler weight prediction. Area ( A ) refers to the surface area of the bird feature under consideration.…”

Section: Role Of Emerging Technologies In Poultry Health Managementmentioning

confidence: 99%

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A Literature Review on the Role of Internet of Things, Computer Vision, and Sound Analysis in a Smart Poultry Farm

Goyal,

Yadav,

Mukherjee

2024

ACS Agric. Sci. Technol.

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The rapid growth of human population and frequent changes in environmental conditions pose challenges in providing nutrient-rich food to the current population. The demand for poultry products is increasing exponentially as they are a good source of low-cost proteins. Ensuring the well-being of birds and delivering nutritious poultry products is essential for both current and future food security needs and sustainable agriculture. This article is motivated by the potential of emerging digital technologies in agriculture to provide innovative solutions to challenges faced by farmers, particularly in poultry farming. It aims to shed light on various issues encountered by farmers like diseases on poultry farms and offers valuable insights for researchers seeking to address these challenges using the advancements in digital technologies. Precision farming in poultry involves leveraging technologies such as the internet of things, artificial intelligence, and edge computing to enhance animal health management. A smart poultry farm maintains the farm environment and detects diseases at early stage in the chickens. The farm resources are utilized optimally in the smart poultry farm. This research work provides a systematic literature review of intelligent systems designed for (i) poultry birds' health and welfare management using technologies like the internet of things (IoT), artificial intelligence, cloud computing, and edge computing and (ii) early disease detection, estimation of weight, and feeding behavior of birds with the use of computer vision and vocalization analysis. This article also explores various sensors employed in the development of IoT infrastructure. It has been demonstrated in the literature that modern digital technologies automate the management operations of poultry farms. As a result, the farmer's income increases, and high-quality food products are available to people at low cost.

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“…Computer vision has been used in various fields of life, such as agriculture, animal husbandry, health, smart cities, and others ( Pratondo et al, 2014 ; Rizqyawan et al, 2020 ). In agriculture and animal husbandry, the use of computer vision for the classification and detection of various objects has been widely practiced ( Abd Aziz et al, 2021 ; Thai, Nguyen & Pham, 2021 ). The detection and classification of similar objects are challenging tasks for researchers to provide the best possible accuracy.…”

Section: Introductionmentioning

confidence: 99%

Classification ofZophobas morioandTenebrio molitorusing transfer learning

Pratondo

Bramantoro

2022

PeerJ Computer Science

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Zophobas Morio and Tenebrio Molitor are popular larvae as feed ingredients that are widely used by animal lovers to feed reptiles, songbirds, and other poultry. These two larvae share a similar appearance, however; the nutritional ingredients are significantly different. Zophobas Morio is more nutritious and has a higher economic value compared to Tenebrio Molitor. Due to limited knowledge, many animal lovers find it difficult to distinguish between the two. This study aims to build a machine learning model that is able to distinguish between the two. The model is trained using images that are taken from a standard camera on a mobile phone. The training is carried on using a deep learning algorithm, by adopting an architecture through transfer learning, namely VGG-19 and Inception v3. The experimental results on the datasets show that the accuracy rates of the model are 94.219% and 96.875%, respectively. The results are quite promising for practical use and can be improved for future works.

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A Review on Computer Vision Technology for Monitoring Poultry Farm—Application, Hardware, and Software

Cited by 20 publications

References 92 publications

Keypoint Detection for Injury Identification during Turkey Husbandry Using Neural Networks

Keypoint Detection for Injury Identification during Turkey Husbandry Using Neural Networks

A Literature Review on the Role of Internet of Things, Computer Vision, and Sound Analysis in a Smart Poultry Farm

Classification ofZophobas morioandTenebrio molitorusing transfer learning

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