Joint Feature Target Detection Algorithm of Beak State Based on YOLOv5

Zu, Linan; Chu, Xiaoyu; Wang, Qiaomei; Ju, Young‐Kyu; Zhang, Mingyue

doi:10.1109/access.2023.3275432

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…The most commonly used machine learning and deep learning models for image classification in poultry farming are SVM, CNN, and ANN . Researchers in refs , , and have used pretrained models (like faster R-CNN, YOLOv X , ResNet50, and Single Shot MultiBox Detector (SSD)) for image classification under computer vision to identify sick birds and weight of the birds. Degu et al have designed a smartphone-based chicken disease detection system with the use of YOLOv3 and ResNet50 models.…”

Section: Role Of Emerging Technologies In Poultry Health Managementmentioning

confidence: 99%

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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Section: Role Of Emerging Technologies In Poultry Health Managementmentioning

confidence: 99%

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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Solar Powered Thermoelectric Air Conditioning for Temperature Control in Poultry Incubators

Cheepati,

Balal

2024

Sustainability

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Proper air conditioning is crucial for hatching, growing, and reproducing poultry chickens. The existing methods are often costly and only viable for some chicken farmers. This paper presents a novel solar-powered thermoelectric module that utilizes the Peltier effect for efficient cooling and heating in poultry incubators. The proposed system consists of a Peltier module with cool and hot junctions, powered by a solar panel through a charge controller and battery. The cool junction is located in the chicken-breeding and reproduction unit, while the hot junction is situated in the egg-incubation unit. Temperature controllers maintain the required temperatures of 35–40 °C for the egg-hatching and 10–24 °C for the chicken-growing units. The experimental results demonstrate the system’s effectiveness in maintaining the desired temperatures. This solar-powered thermoelectric air conditioning system offers advantages over traditional methods, including lower energy consumption, reduced costs, and eco-friendliness. It has the potential to benefit off-grid poultry farmers and reduce energy bills for existing chicken farms. The mathematical modeling, load calculations, and prototype results show that the proposed system is best suited for providing the required cooling and heating effects in poultry incubators. This research represents a significant step forward in temperature control for poultry incubators and could revolutionize poultry farming practices, especially in remote locations with limited electricity access.

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Prediction of Chicken Diseases by Transfer Learning Method

BINGOL,

BİLGİN

2023

International Scientific and Vocational Studies Journal

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With the development of computing technologies, artificial intelligence is used in a wide range of areas, from engineering to healthcare. In this study, it was aimed to predict chicken diseases with transfer learning. For this purpose, a ready-made data set was studied. This data set contains fecal photographs of healthy chickens diagnosed with Coccidiosis, Newcastle and Salmonella diseases. The data set has been subjected to necessary pre-processing such as size readjustment. Subsequently, the data set, which was then subjected to pre-processing, was divided into 70% and 30% as training and testing. To solve the disease classification problem, a network was created by adding fully connected layers to ResNet50, InceptionV3, InceptionResNetV2, Xception and MobileNetV2 architectures. The weights of the architectures mentioned in these networks were selected as ImageNet and were not trained. Then, networks containing these architectures were trained using the training data set. The trained networks were validated with the test data set and accuracy rates of 32.7%, 80.6%, 85.2%, 89.2% and 90.7% were obtained, respectively. According to these results, MobileNetV2 was used in the proposed artificial neural network architecture since the best result was calculated using the MobileNetV2 architecture. The proposed artificial neural network architecture was trained with the same training set and validation was carried out with the same test data set. After these procedures, the true prediction rate of the proposed architecture for the test data set was calculated as 92.1%. Also, F1 score of the proposed architecture was measured 0.923. Additionally, thanks to the deconvolution layer used in the proposed architecture, network sizes have been reduced by approximately 50%. Thanks to this reduction, the training time is shortened and it becomes easier to implement it on embedded systems in future studies. As a result, the diseases of chickens were predicted largely accurately with the transfer learning method.

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Joint Feature Target Detection Algorithm of Beak State Based on YOLOv5

Cited by 3 publications

References 19 publications

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

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

Solar Powered Thermoelectric Air Conditioning for Temperature Control in Poultry Incubators

Prediction of Chicken Diseases by Transfer Learning Method

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