Research Progress in the Early Warning of Chicken Diseases by Monitoring Clinical Symptoms

He, Pengguang; Chen, Zhonghao; Yu, Han‐Qing; Hayat, Khawar; He, Yefan; Pan, Jinming; Lin, Hongjian

doi:10.3390/app12115601

Cited by 20 publications

(13 citation statements)

References 85 publications

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“…However, given the increasing importance of the early detection of diseases or infections, a multi-class classification approach is becoming more relevant. For example, in a recent study [28], researchers identify the eight prevalent diseases (such as Avian influenza, Infectious bursa disease, Pullorum disease, etc.) that lead to diarrhea in chickens and highlight visual distinctive dropping characteristics associated with each disease.…”

Section: Related Workmentioning

confidence: 99%

Towards Early Poultry Health Prediction through Non-Invasive and Computer Vision-Based Dropping Classification

Nakrosis,

Paulauskaite-Taraseviciene,

Raudonis

et al. 2023

Animals

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The use of artificial intelligence techniques with advanced computer vision techniques offers great potential for non-invasive health assessments in the poultry industry. Evaluating the condition of poultry by monitoring their droppings can be highly valuable as significant changes in consistency and color can be indicators of serious and infectious diseases. While most studies have prioritized the classification of droppings into two categories (normal and abnormal), with some relevant studies dealing with up to five categories, this investigation goes a step further by employing image processing algorithms to categorize droppings into six classes, based on visual information indicating some level of abnormality. To ensure a diverse dataset, data were collected in three different poultry farms in Lithuania by capturing droppings on different types of litter. With the implementation of deep learning, the object detection rate reached 92.41% accuracy. A range of machine learning algorithms, including different deep learning architectures, has been explored and, based on the obtained results, we have proposed a comprehensive solution by combining different models for segmentation and classification purposes. The results revealed that the segmentation task achieved the highest accuracy of 0.88 in terms of the Dice coefficient employing the K-means algorithm. Meanwhile, YOLOv5 demonstrated the highest classification accuracy, achieving an ACC of 91.78%.

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Section: Related Workmentioning

confidence: 99%

Towards Early Poultry Health Prediction through Non-Invasive and Computer Vision-Based Dropping Classification

Nakrosis,

Paulauskaite-Taraseviciene,

Raudonis

et al. 2023

Animals

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“…Penelitian [11] mengusulkan penggunaan teknologi untuk melakukan proses deteksi terhadap gejala penyakit serta dapat memantau status kesehatan unggas secara terus menerus dan otomatis, serta berpotensi membantu dalam proses pengambilan keputusan peringatan dini terhadap kesehatan unggas. Pada saat ini proses deteksi penyakit unggas dapat dilakukan berdasarkan karakteristik suara, karakteristik suhu tubuh, karakteristik feses, karakteristik produksi, karakteristik aktivitas dan karakteristik postur tubuh unggas.…”

Section: Pendahuluanunclassified

Penerapan model InceptionV3 dalam klasifikasi penyakit ayam

Muhammad Salimy Ahsan,

Kusrini Kusrini,

Dhani Ariatmanto

2023

JNNLK

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Chicken disease is one of the problems that can have a very significant impact on chicken farmers, in addition to having an impact on the farm itself, chicken disease can also have an impact on the surrounding environment. Lack of knowledge about the symptoms and diseases that occur in chickens, makes some chicken breeders treat and treat diseases in a traditional way. This method often takes a long time and is prone to errors. In this study, technology will be used to classify chicken diseases by utilizing a deep learning model from the Convolutional Neural Network (CNN) architecture, namely InceptionV3. In carrying out the process of classifying chicken diseases, using a dataset of chicken feces images with a number of 8067 Healthy, Salmonella, Coccidiosis, and Newcastle disease. In the research process, three experimental scenarios were carried out using 20 epochs, 50 epochs and 100 epochs. From the experimental results, using a value of 100 epochs produces the highest accuracy value with a value of 94.05%.

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“…This information is then used to improve their health condition, and ultimately, enhance production efficiency and profitability. In addition, an effective system for detecting and analyzing chicken behavior contributes in early disease detection, preventing outbreaks [1], ensuring food safety, protecting public health [2], and ensuring the quality of poultry production [3]. Hence, having a reliable system for monitoring chicken behavior is essential for sustainable poultry farming and ensuring the well-being of the birds.…”

Section: Introductionmentioning

confidence: 99%

Chicken Behavior Analysis for Surveillance in Poultry Farms

Mohialdin¹,

Elbarrany²,

Atia³

2023

IJACSA

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Poultry farming is an important industry that provides food for a growing population. However, the welfare of the birds is a major concern, as poor living conditions leads to abnormal behavior that affects the health and productivity of the flock. In order to monitor and improve the welfare of the birds, it is important to have a surveillance system in place that monitors the behavior of the chickens and alert farmers to potential issues. This paper reviews the current state of the art in behavior analysis for surveillance in poultry farms and discuss potential future directions for research in this area. This paper presents a computer-vision-based system that detects and monitors the behaviors of the chickens in poultry farms. The system classifies three behaviors which are eating, walking and sleeping. The system takes videos as input and then classifies the behavior of the chicken. The proposed system produces an accuracy of 94.7% using Light Gradient Boosting Machine on a collected data-set of chickens, and a 98.4% accuracy on a benchmarked Human Activity Recognition data-set.

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Research Progress in the Early Warning of Chicken Diseases by Monitoring Clinical Symptoms

Cited by 20 publications

References 85 publications

Towards Early Poultry Health Prediction through Non-Invasive and Computer Vision-Based Dropping Classification

Towards Early Poultry Health Prediction through Non-Invasive and Computer Vision-Based Dropping Classification

Penerapan model InceptionV3 dalam klasifikasi penyakit ayam

Chicken Behavior Analysis for Surveillance in Poultry Farms

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