TY - GEN
T1 - Practical Mobile Based Services for Identification of Chicken Diseases from Fecal Images
AU - Silapachote, Piyanuch
AU - Srisuphab, Ananta
AU - Damkham, Waris
AU - Korkiattrakool, Pattanan
AU - Songdechakaivut, Kanokpitch
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Poultry farming is a vital component in a food chain. Health of chickens in the farms directly plays an important role in both quality and safety of all chicken products. Accurate diagnosis of poultry diseases from chicken feces using standardized polymerase chain reaction is relatively very expensive, making it rather challenging especially for small local farms. To tackle this, we developed a practical mobile-based service that aimed to provide local farmers an easy-to-use tool capable of preliminary identification of common diseases chickens may have from their fecal images. Our system was deployed as an official Line account on a familiar Line application that most farmers have on their mobile phones. Trained and evaluated on a large open database of chicken fecal images, it achieved a segmentation mean average precision of 86.49 % and classification accuracy of 95.93 %. Ours also, with high confidence, correctly identify healthy images taken by local farmers.
AB - Poultry farming is a vital component in a food chain. Health of chickens in the farms directly plays an important role in both quality and safety of all chicken products. Accurate diagnosis of poultry diseases from chicken feces using standardized polymerase chain reaction is relatively very expensive, making it rather challenging especially for small local farms. To tackle this, we developed a practical mobile-based service that aimed to provide local farmers an easy-to-use tool capable of preliminary identification of common diseases chickens may have from their fecal images. Our system was deployed as an official Line account on a familiar Line application that most farmers have on their mobile phones. Trained and evaluated on a large open database of chicken fecal images, it achieved a segmentation mean average precision of 86.49 % and classification accuracy of 95.93 %. Ours also, with high confidence, correctly identify healthy images taken by local farmers.
KW - chicken diseases
KW - fecal images
KW - mobile services
UR - https://www.scopus.com/pages/publications/105000415320
U2 - 10.1109/TENCON61640.2024.10902790
DO - 10.1109/TENCON61640.2024.10902790
M3 - Conference contribution
AN - SCOPUS:105000415320
T3 - IEEE Region 10 Annual International Conference, Proceedings/TENCON
SP - 108
EP - 111
BT - Proceedings of the IEEE Region 10 Conference 2024
A2 - Luo, Bin
A2 - Sahoo, Sanjib Kumar
A2 - Lee, Yee Hui
A2 - Lee, Christopher H T
A2 - Ong, Michael
A2 - Alphones, Arokiaswami
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE Region 10 Conference, TENCON 2024
Y2 - 1 December 2024 through 4 December 2024
ER -