Classification of Infectious Diseases in Chickens Based on Feces Images Using Deep Learning

Kholil, Moch.; Waspada, Heri Priya; Akhsani, Rafika

doi:10.1109/ieit56384.2022.9967850

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…Utilizing Convolutional Neural Networks (CNN) implemented in Keras/TensorFlow, Study of Moch. Kholil et al [29] achieved an impressive accuracy rate of 95.28% in accurately predicting the classification of infectious diseases suffered by chickens. The research highlights the effectiveness of CNNs in disease classification tasks, particularly in the context of poultry farming.…”

Section: Related Workmentioning

confidence: 99%

Improving Chicken Disease Classification Based on Vision Transformer and Combine with Integrated Gradients Explanation

Luong,

Nguyen

2024

IJACSA

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Chicken diseases are an important problem in the livestock industry, affecting the health and production performance of chicken flocks worldwide. These diseases can seriously damage the health of chickens, reduce egg production, or increase mortality, causing great economic losses to farmers. Therefore, detecting and preventing diseases in chickens is a top concern in the livestock industry, to ensure the health and sustainable production of chicken flocks. In recent years, advances in machine learning techniques have shown promise in solving challenges related to image diagnosis and classification. Leveraging the power of machine learning models, we propose the ViT16 model for disease classification in chickens, demonstrating its potential in assisting healthcare professionals to diagnose chicken flocks more effectively. In this study, ViT16 demonstrated its potential and strengths when compared with 5 models in the CNN architecture and ViT32 in the ViT architecture in the task of classifying chicken disease images with an accuracy of 99.25% -99.75% -100% -98.25% in four experimental scenarios with our enhanced dataset and fine-tuning. These results were generated from transfer learning and model tuning on an augmented dataset consisting of 8067 images classified into four classes: Coccidiosis, New Castle Disease, Salmonella, and Healthy. Furthermore, the Integrated Gradients explanation has an important role in increasing the transparency and understanding of the image classification model, thereby improving and optimizing model performance. The performance evaluation of each model is done through in-depth analysis, including metrics such as precision, recall, F1 score, accuracy, and confusion matrix.

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Section: Related Workmentioning

confidence: 99%

Improving Chicken Disease Classification Based on Vision Transformer and Combine with Integrated Gradients Explanation

Luong,

Nguyen

2024

IJACSA

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“…Using the chicken manure data set and the ResNeXt50 network model after implementing a mixed attention strategy [17]. The proportion of pictures correctly predicted based on the categorization of viral disorders experienced by hens using DL predicated on CNN [18]. Using a wildlife database, researchers have developed a programme that can accurately identify and categorise poultry illnesses in their early stages [19].…”

Section: Literature Reviewmentioning

confidence: 99%

Mathematical Formulation of Deep Learning Model for Poultry Disease Classification using EfficientNet- B3 CNN Model

Ajay G. Barsagade

2024

cana

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The agricultural industry, and particularly the poultry farming industry, is under pressure to increase output levels due to the growing human demand for animal products. There can be substantial economic losses and widespread chicken mortality as a consequence of an in-crease in infectious disease transmission caused by expanded poultry production. When compared to conventional ways for poultry disease identification, manual methods are time-consuming, labour-intensive, and error-prone. Also, experts' knowledge is usually necessary for making sense of the results. These restrictions raise the danger of the disease spreading across the flock and make it more difficult to diagnose diseases in a timely manner, both of which can have devastating effects. For a long time now, farmers have relied on specialists to identify and diagnose chicken illnesses. Consequently, many domesticated birds end up in the hands of farmers who suffer from either unreliable specialists or delayed diagnosis. The most prevalent chicken illnesses may be quickly recognised from pictures of chicken drop-pings using the techniques that are already accessible from artificial intelligence (AI) based on computer vision.In this paper, a deep learning approach offered that uses a pre-trained Convolution Neural Networks (CNN) model to determine which of the three categories best describes chicken excrement and offer a method for identifying and categorising poultry illnesses. The Effi-cientNet-B3 model was utilised in the development of the system. Coccidiosis, Salmonella, New Castle Disease and Healthy were the four health problems that were classified using the segmented picture by the deep learning model. The models were trained using standard benchmark database images of excrement from chickens. The outcomes of experiment demonstrate that the proposed method for identifying and categorizing chicken illnesses may accurately identify three prevalent poultry diseases. Consequently, this approach has the potential to be an invaluable resource for farm veterinarians and poultry producers.

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“…Chicken egg waste is in the form of egg shells in abundant quantities but can damage the environment and cause pollution [7]. Based on the amount of egg consumption in Indonesia, in 2022 the number of egg shells will be 5.6 million tons [8]. Chicken egg shells are a source of calcium and protein that can be utilized by the human body [7].…”

Section: Introductionmentioning

confidence: 99%

Improvement of physical and sensory quality of beef patties with the addition of chicken eggshells nanopowder

Evanuarini,

Susilo,

Yanuhar

et al. 2023

Livest. Anim. Res.

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<p class="MDPI17abstract"><strong>Objective:</strong> The purpose of this study was to investigate the effect of adding chicken eggshells nanopowder for the physical quality and sensory evaluation of beef patties.</p><p class="MDPI17abstract"><strong>Methods:</strong> The method used in this study was a laboratory experiment with a completely randomized design with 5 treatments: without adding chicken eggshells nanopowder as a control, adding 0.1%, 0.3%, 0.5% and 0.7% chicken eggshells nanopowder (w/w) with 5 repetitions. Data were analyzed by analysis of variance, if there was a significant or very significant difference it was continued with Duncan's multiple range test.</p><p class="MDPI17abstract"><strong>Results:</strong> Based on the research results, it was found that the addition of chicken eggshells nanopowder to beef patties had a very significant effect (P<0.01%) on moisture content, water holding capacity, cooking loss, texture, and sensory evaluation.</p><p class="MDPI17abstract"><strong>Conclusions:</strong> The addition of 0.7% chicken eggshells nanopowder can reduce moisture content, increase water holding capacity, reduce cooking loss, improve texture, and sensory quality acceptable to panelists.</p><div style="mso-element: comment-list;"><div style="mso-element: comment;"><div id="_com_2" class="msocomtxt"></div></div></div>

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Classification of Infectious Diseases in Chickens Based on Feces Images Using Deep Learning

Cited by 8 publications

References 13 publications

Improving Chicken Disease Classification Based on Vision Transformer and Combine with Integrated Gradients Explanation

Improving Chicken Disease Classification Based on Vision Transformer and Combine with Integrated Gradients Explanation

Mathematical Formulation of Deep Learning Model for Poultry Disease Classification using EfficientNet- B3 CNN Model

Improvement of physical and sensory quality of beef patties with the addition of chicken eggshells nanopowder

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