Correction: Li et al. Mitigation of Airborne PRRSV Transmission with UV Light Treatment: Proof-of-Concept. Agriculture 2021, 11, 259

Li, Peiyang; Kozieł, Jacek A.; Zimmerman, Jeffrey J.; Zhang, Jianqiang; Cheng, Ting‐Yu; Yim-Im, Wannarat; Jenks, William S.; Lee, Myeongseong; Chen, Baitong; Hoff, Steven J.

doi:10.3390/agriculture12050680

Cited by 2 publications

(3 citation statements)

References 2 publications

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“…A similar method of using deep learning image categorization based on fecal pictures, [9] a proof-of-concept chicken health assessment system was created. When it came to distinguishing between birds that were healthy and those that had Newcastle disease, salmonella, and coccidiosis.…”

Section: Poultry Disease Identification In Fecal Images Using Vision ...mentioning

confidence: 99%

Poultry Disease Identification In Fecal Images Using Vision Transformer

2023

MCAES

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Early diagnosis and control of diseases assume critical importance in the poultry farming industry. While the large sized scale poultry farms may be able to afford in-house veterinary support, the small and medium-sized operations may not be able to have theirown diagnosis services, making it all the more critical for such farms to make use of the emerging technologies to assist them in the early diagnosis and control of their poultry farms. Prompt and accurate identification of poultry diseases without delay assumes paramount importance, as the delays can be devastating, resulting in significant economic losses. To confront this pressing issue, our research presents a novel and highly practical method with the use of Deep learning models. We harness the capabilities of Deep learning and image analysis to facilitate quick diagnosis of diseases. The paper has attempted to analyze the fecal images and train a range of models such as GoogleNet, Resenet18, ShuffleNet, SqueezeNet, and Vision Transform, achieving a peak test accuracy of 97.62%. Towards this, the study has made use of an extensive dataset of over half a million images and in the process accounted for the challenging environmental conditions such as dust on cameras and lower quality of images. The model can automatically identify common poultry diseases such as Coccidiosis, New Castle Disease, and Salmonella through the analysis of fecal images. The study is an attempt to bridge the gap between technological advancements and the day-to-day requirements in disease detection requirements of the poultry farming community, which may in turn support the poultry industry in better sustainability, economics, and enhanced welfare of the birds. The dataset used in this study has been made available Poultry Pathology Visual Dataset (kaggle.com).

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Section: Poultry Disease Identification In Fecal Images Using Vision ...mentioning

confidence: 99%

Poultry Disease Identification In Fecal Images Using Vision Transformer

2023

MCAES

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“…Low-density polyethylene (LDPE) is commonly used as greenhouse film due to its good light transmittance and excellent mechanical properties. [20] However, the thermal retention property of LDPE is very poor, as it is F I G U R E 1 0 IR effectiveness of UT film and calcined-kaolin composite film [21] F I G U R E 1 1 Energy costs -CUT film against reference film for different crops [20] transparent to FIR and heat dissipates rapidly via radiation. Two traditional methods are used to improve FIR blocking capability of LDPE.…”

Section: Ultrathermic Filmsmentioning

confidence: 99%

“…[16][17][18][19] Meanwhile, the fillers may cause changes in other properties of the polymer film, such as mechanical performance and rheological behavior. [20] In order to distinguish different light on the spectrum, the most of radiation is divided into three groups as shown in Figure 3: ultraviolet (UV, wavelength between 10 and 400 nm), [21] visible light (VIS, wavelength between 400 and 780 nm) and infrared radiation (IR, wavelength between 780 nm and 1 mm). [22] Besides, radiation whose wavelengths are range from 7 to 14 μm is called Far-Infra-Red (FIR) in this article due to its high monochromatic radiant at normal temperature.…”

Section: Introductionmentioning

confidence: 99%

Micro/nano particles reinforced polymer composites as greenhouse films for heat retention purposes

Yang

Wang

2022

Polymer Composites

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The aim and objective of this review is to intensively summarize composite film materials that can achieve thermal radiation insulation without affecting visible light transmittance, in order to reduce the heat loss in greenhouses and thus save energy. Eight kinds of composite films, which could be used for radiation insulation, were finally determined and intensively reviewed by searching the key words including insulation film, insulation agent, and infrared insulation. These materials can be divided into two categories: silica/poly and zinc oxide/poly composites. The radiation thermal insulation property and light transmission property of these materials were experimentally tested by spectrometry. The key finding of this article includes that phonon resonance and Anderson localization could be used to block thermal infrared radiation by generalizing material properties. The conclusion of this review is that those materials have at least a 10% increase in infrared thermal radiation blocking, but only a small decrease in visible light transmittance.

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Correction: Li et al. Mitigation of Airborne PRRSV Transmission with UV Light Treatment: Proof-of-Concept. Agriculture 2021, 11, 259

Abstract: We would like to make corrections to the original article [...]

Cited by 2 publications

References 2 publications

Poultry Disease Identification In Fecal Images Using Vision Transformer

Poultry Disease Identification In Fecal Images Using Vision Transformer

Micro/nano particles reinforced polymer composites as greenhouse films for heat retention purposes

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