Practical Mobile Based Services for Identification of Chicken Diseases from Fecal Images

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.

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Publication Details

Published
2024-12-01
DOI
https://doi.org/10.1109/tencon61640.2024.10902790
Primary Topic
Smart Agriculture and AI
Type
article
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article

Practical Mobile Based Services for Identification of Chicken Diseases from Fecal Images

Smart Agriculture and AI
article

Practical Mobile Based Services for Identification of Chicken Diseases from Fecal Images

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Abstract

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.

Mahidol University (TH)
Zero hunger
Openalex Percentile: Top 83%
Smart Agriculture and AI
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