Detection of Chicken Disease Based on Day-Age Using Pre Trained Model of CNN

Sreenivasulu, K.; Khan, H. Aini Sosan; Damini, K.; Akhila, M.; Bharathi, G.

doi:10.1007/978-3-031-44084-7_2

Cited by 2 publications

(1 citation statement)

References 20 publications

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“…Although this can reduce the computational consumption, the quality of the feature convolution kernel needs to be considered. ResNet [30] has a wide range of applications in agriculture due to its excellent model performance [31][32][33]. ImageNet is a large-scale dataset, and by pretraining on this dataset, a model can be given good feature extraction capability [34].…”

Section: Feature Convolution Kernel Generation Modulementioning

confidence: 99%

DFCCNet: A Dense Flock of Chickens Counting Network Based on Density Map Regression

Lv,

Wang,

Peng

et al. 2023

Animals

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With the development of artificial intelligence, automatically and accurately counting chickens has become a reality. However, insufficient lighting, irregular sizes, and dense flocks make this a challenging task. The existing methods cannot perform accurate and stable counting. In this article, a dense flock of chickens counting network (DFCCNet) is proposed based on density map regression, where features from different levels are merged using feature fusion to obtain more information for distinguishing chickens from the background, resulting in more stable counting results. Multi-scaling is used to detect and count chickens at various scales, which can improve the counting accuracy and ensure stable performance for chickens of different sizes. Feature convolution kernels are adopted to convolve feature maps, which can extract more accurate target information, reduce the impact of occlusion, and achieve more reliable and precise results. A dataset of dense flocks of chickens (namely Dense-Chicken) has been collected and constructed, which contains 600 images of 99,916 chickens, with labeled points and boxes. It can be accessed by researchers as benchmark data. The proposed method was compared with some state-of-the-art algorithms, to validate its effectiveness. With its robustness being verified by counting in three kinds of density situations, with the mean absolute error being 4.26, 9.85, and 19.17, respectively, and a speed of 16.15 FPS. DFCCNet provides an automatic and fast approach to counting chickens in a dense farming environment. It can be easily embedded into handheld devices for application in agricultural engineering.

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Section: Feature Convolution Kernel Generation Modulementioning

confidence: 99%

DFCCNet: A Dense Flock of Chickens Counting Network Based on Density Map Regression

Lv,

Wang,

Peng

et al. 2023

Animals

View full text Add to dashboard Cite

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Automating Poultry Disease Detection using Deep Learning

S. Iwin Thanakumar Joseph

2023

JSCP

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Poultry farming plays a vital role in global food production but the emerging threat of diseases poses significant challenges to both sustainability and food security. In particular, this research study investigates the integration of deep learning techniques to automate the detection of four major poultry diseases—Avian Influenza, Coccidiosis, Newcastle Disease, and Gumboro Disease—from faecal samples. The proposed methodology involves collecting diverse faecal samples, pre-processing the data, and developing a Convolutional Neural Network (CNN) architecture. The CNN layered architecture is designed to extract hierarchical features and learn complex patterns associated with each disease. Through the integration of activation function, Rectified Linear Units (ReLU), the network incorporates non-linearity, enhancing its ability to detect the disease-related features. The faecal samples undergo image enhancement, normalization, and segmentation to ensure suitability for the deep learning model. The performance of the proposed model is evaluated using the performance metrics and achieved an overall accuracy of 98.82% on the training set, 93.22% on the testing set, and 96.65% on the validation set., precision, recall and F1-Score. This research study contributes to the advancement of automated disease detection, offering a potential solution to mitigate the impact of poultry diseases and enhance overall food safety.

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Detection of Chicken Disease Based on Day-Age Using Pre Trained Model of CNN

Cited by 2 publications

References 20 publications

DFCCNet: A Dense Flock of Chickens Counting Network Based on Density Map Regression

DFCCNet: A Dense Flock of Chickens Counting Network Based on Density Map Regression

Automating Poultry Disease Detection using Deep Learning

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