Abstract
Various diagnostic methods are used in chickens, including counting oocytes in the stool or intestinal tract, detecting the virus and using polymerase chain reaction (PCR) procedures, which require multiple diagnoses. The diseases are transmitted through contaminated feed and excrement from infected poultry. As a result of late diagnoses or a lack of credible specialists, many domesticated birds are lost by farmers. The most common ailments affecting chickens can be easily identified in the pictures of chicken drop**s using artificial intelligence and machine learning methods based on computer screening and image analysis. In this paper, a model for early detection and classification of poultry diseases with high accuracy using wildlife database is proposed. The dataset contains 6812 images of four different classes such as healthy chicken, Coccidiosis, Salmonella and Newcastle images is proposed. A deep learning method based on convolutional neural networks (CNN) is used to predict whether chicken faecal image belongs to any of the four categories. The pre-trained DenseNet model, the Inception model and the MobileNet model were used to predict whether chicken faecal belonged to four categories with minimum loss. In comparison with the above-mentioned models, DenseNet method produced the best results with an accuracy of 97% which is recommended for poultry diagnostic application.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bao Y, Lu H, Zhao Q, Yang Z, Xu W (2021) Detection system of dead and sick chickens in large scale farms based on artificial intelligence. Math Biosci Eng 18(5):6117–6135
Bhatnagar N, Ryan D, Murphy R, Enright AM (2020) Trace element supplementation and enzyme addition to enhance biogas production by anaerobic digestion of chicken litter. Energies 13(13):3477
Chen Z, Jiang X (2014) Microbiological safety of chicken litter or chicken litter-based organic fertilizers: a review. Agriculture 4(1):1–29
Fatoba AJ, Adeleke MA (2018) Diagnosis and control of chicken coccidiosis: a recent update. J Parasit Dis 42(4):483–493
Gohm DS, Thür B, Hofmann M (2000) Detection of Newcastle disease virus in organs and faeces of experimentally infected chickens using RT-PCR. Avian Pathol 29(2):143–152
Jacob IJ, Darney PE (2021) Design of deep learning algorithm for IoT application by image based recognition. J ISMAC 3(03):276–290
Kyakuwaire M, Olupot G, Amoding A, Nkedi-Kizza P, Ateenyi Basamba T (2019) How safe is chicken litter for land application as an organic fertilizer? A review. Int J Environ Res Public Health 16(19):3521
Machuve D, Nwankwo E, Mduma N, Mbelwa H, Maguo E, Munisi C (2021) Machine learning dataset for poultry diseases diagnostics. https://doi.org/10.5281/zenodo.4628934
Mageshkumar G, Suthagar S, Tamilselvan K (2018) Performance comparison of adaptive filters for speckle noise reduction in SAR images. In: 2018 international conference on intelligent computing and communication for smart world (I2C2SW). IEEE, pp 195–197
Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. J Big Data 6(1):1–48
Stephens C, Hampson D (1999) Prevalence and disease association of intestinal spirochaetes in chickens in eastern Australia. Avian Pathol 28(5):447–454
Suthagar S, Snegha C, Sureka M, Velmurugan S (2022) Analysis of breast cancer classification using various algorithms. In: 2022 6th international conference on computing methodologies and communication (ICCMC). IEEE, pp 1286–1291
Suthagar S, Tamilselvan K, Balakumar P, Rajalakshmi B, Roshini C (2020) Translation of sign language for deaf and dumb people. Int J Recent Technol Eng 8(5):4369–4372
Suthagar S, Tamilselvan K, Priyadharshini M, Nihila B (2021) Determination of apple, lemon, and banana ripening stages using electronic nose and image processing. In: Innovations in cyber physical systems. Springer, pp 755–769
Weldekidan H, Strezov V, Li R, Kan T, Town G, Kumar R, He J, Flamant G (2020) Distribution of solar pyrolysis products and product gas composition produced from agricultural residues and animal wastes at different operating parameters. Renew Energy 151:1102–1109
Zhang Z, Han Y (2020) Detection of ovarian tumors in obstetric ultrasound imaging using logistic regression classifier with an advanced machine learning approach. IEEE Access 8:44999–45008
Zhuang X, Bi M, Guo J, Wu S, Zhang T (2018) Development of an early warning algorithm to detect sick broilers. Comput Electron Agric 144:102–113
Zhuang X, Zhang T (2019) Detection of sick broilers by digital image processing and deep learning. Biosyst Eng 179:106–116
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Suthagar, S., Mageshkumar, G., Ayyadurai, M., Snegha, C., Sureka, M., Velmurugan, S. (2023). Faecal Image-Based Chicken Disease Classification Using Deep Learning Techniques. In: Smys, S., Kamel, K.A., Palanisamy, R. (eds) Inventive Computation and Information Technologies. Lecture Notes in Networks and Systems, vol 563. Springer, Singapore. https://doi.org/10.1007/978-981-19-7402-1_64
Download citation
DOI: https://doi.org/10.1007/978-981-19-7402-1_64
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-7401-4
Online ISBN: 978-981-19-7402-1
eBook Packages: EngineeringEngineering (R0)