Using Otsu's Method for Image Segmentation to Determine the Particle Density, Surface Coverage and Cluster Size Distribution of 3 nm Si Nanoparticles

Ashraf, Juveiriah M.; Hadi, Sabina Abdul; Rezk, Ayman; Madjid, Nadya Abdel; Alnaqbi, Wafa; Alhammadi, Aisha; Nayfeh, Ammar

doi:10.1109/tnano.2021.3116185

Cited by 4 publications

(3 citation statements)

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“…This preprocessing step is undertaken to transform the raw images into a suitable format for subsequent stages. The Otsu, 59 image thresholding, 78 Gaussian filter, 67 binarization, 79 and morphological filtering 67 methods have been used by researchers as preprocessing methods in computer vision for poultry farming.…”

Section: Computer Vision For Health Management Of Birdsmentioning

confidence: 99%

“…The raw images provided by the data acquisition software go for image preprocessing in the first step to prepare raw images into a suitable form for the next stage. The Otsu, image thresholding, Gaussian filter, binarization, and morphological filtering methods have been used by researchers as preprocessing methods in computer vision for poultry farming. Image segmentation separates images into multiple segments based on similar attributes.…”

Section: Role Of Emerging Technologies In Poultry Health Managementmentioning

confidence: 99%

“…The raw images provided by the data acquisition software go for image preprocessing in the first step to prepare raw images into a suitable form for the next stage. The Otsu, 59 image thresholding, 78 Gaussian filter, 67 binarization, 79 and morphological filtering 67 5 summarizes the different software used for computer vision in poultry farming for the health and welfare management of birds.…”

Section: Computer Vision For Health Management Of Birdsmentioning

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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