Two-Phase Sensor Decision: Machine-Learning for Bird Sound Recognition and Vineyard Protection

Cinkler, Tibor; Nagy, Krisztina; Simon, Csaba; Vida, Rolland; Rajab, Husam

doi:10.1109/jsen.2021.3134817

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…Mahdavian et al have used SVM to identify the birds infected by bronchitis and Newcastle disease. Cinkler et al have used SVM-based two-step classifiers to identify the sounds of birds on agricultural land and play a sound when birds are identified near the crops. In bioacoustics, SVM was used to classify healthy and unhealthy birds − .…”

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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“…Compared with the manual The associate editor coordinating the review of this manuscript and approving it for publication was Chuan Li. observation method, infrared camera monitoring method and mark-and-recapture method, the bird sounds recognition method is efficient and stable. With the rapid development of artificial intelligence [14], [15], the use of deep learning technology to identify birds has become the mainstream of technology [16], [17], [18].…”

Section: Introductionmentioning

confidence: 99%

Wild Bird Species Identification Based on a Lightweight Model with Frequency Dynamic Convolution

et al. 2023

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The sound recognition technology is used to count bird speciespopulation to provide accurate data for population ecology and conservation biology research. Bird species can be identified by spectrogram of bird's sounds. The existing Convolutional Neural Network(CNN) is not good at mining the relationship between features in time-frequency tasks, and the existing CNN has more parameters and complicated calculation, so existing models are not suitable for deployment in an actual field environment. In order to fill this gap, this paper proposes a lightweight model with frequency dynamic convolution for bird species identification. We use frequency dynamic convolution innovatively to better capture features of bird sounds at different frequencies. First of all, we replaced two-dimensional convolution with frequency-dynamic convolution in order to achieve not-shift invariance of the bird sound's spectrogram, so that we can effectively capture the feature differences of spectrogram in different frequency bands. Then, we replaced part of the Squeeze and Excitation(SE) attention mechanism with the Coordinate Attention(CA) attention mechanism in order to get more comprehensive global information. Finally, the feature fusion module was used to fuse the local and global features. In addition, we built a datasets containing 160 bird sounds, improving the generalization ability of the model. The experimental results show that our model has good generalization ability and is superior to the existing Lightweight CNN, and obtained better results in top1 accuracy and top5 accuracy.

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A hierarchical birdsong feature extraction architecture combining static and dynamic modeling

Wang

Chen

et al. 2023

Ecological Indicators

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Two-Phase Sensor Decision: Machine-Learning for Bird Sound Recognition and Vineyard Protection

Cited by 4 publications

References 16 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

Wild Bird Species Identification Based on a Lightweight Model with Frequency Dynamic Convolution

A hierarchical birdsong feature extraction architecture combining static and dynamic modeling

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