IoT and Edge AI for Quail Environmental Monitoring and Stress-Related Behaviour Classification

Inna Novianty; Ahmad Fauzan; Daffa Ardyana Eka Putra; Muhammad Fadhil  Al Faruq; Muhammad Faza  Elrahman; Radyanka Irza Pramono; Raqhim Putra Al Rusdi; Zulvian Hardhan; Lathifunnisa Fathonah; Faldiena Marcelita; Gema Parasti Mindara; Shelvie Nidya Neyman

doi:10.58797/cser.040105

Authors

Inna Novianty Computer Engineering Technology, Vocational School, IPB University, Jl. Kumbang No. 14, Bogor 16128, Indonesia
Ahmad Fauzan Computer Engineering Technology, Vocational School, IPB University, Jl. Kumbang No. 14, Bogor 16128, Indonesia
Daffa Ardyana Eka Putra Computer Engineering Technology, Vocational School, IPB University, Jl. Kumbang No. 14, Bogor 16128, Indonesia
Muhammad Fadhil Al Faruq Computer Engineering Technology, Vocational School, IPB University, Jl. Kumbang No. 14, Bogor 16128, Indonesia
Muhammad Faza Elrahman Computer Engineering Technology, Vocational School, IPB University, Jl. Kumbang No. 14, Bogor 16128, Indonesia
Radyanka Irza Pramono Computer Engineering Technology, Vocational School, IPB University, Jl. Kumbang No. 14, Bogor 16128, Indonesia
Raqhim Putra Al Rusdi Computer Engineering Technology, Vocational School, IPB University, Jl. Kumbang No. 14, Bogor 16128, Indonesia
Zulvian Hardhan Computer Engineering Technology, Vocational School, IPB University, Jl. Kumbang No. 14, Bogor 16128, Indonesia
Lathifunnisa Fathonah Computer Engineering Technology, Vocational School, IPB University, Jl. Kumbang No. 14, Bogor 16128, Indonesia
Faldiena Marcelita Computer Engineering Technology, Vocational School, IPB University, Jl. Kumbang No. 14, Bogor 16128, Indonesia
Gema Parasti Mindara Computer Engineering Technology, Vocational School, IPB University, Jl. Kumbang No. 14, Bogor 16128, Indonesia
Shelvie Nidya Neyman Computer Science, School of Data Science, Mathematics, and Informatics, IPB University, Jl. Meranti Wing 20 Level 5, Bogor 16680, Indonesia

DOI:

https://doi.org/10.58797/cser.040105

Keywords:

computer vision, edge AI, Internet of Things, quail behaviour, technology-enhanced learning

Abstract

Temperature and humidity readings can show changes inside a quail cage, but they do not tell us how the birds are responding to those conditions. This study developed a low-cost monitoring system that combines environmental sensing with image-based analysis of quail behaviour. A DHT22 sensor measured cage temperature and humidity, while an ESP32 collected and transmitted the readings. A Raspberry Pi 4 processed images captured by a USB camera and ran a Convolutional Neural Network (CNN) based on MobileNet to classify quail behaviour into three operational categories: Normal, Stress, and Aggressive. Environmental readings and classification results were sent through MQTT, stored in a MySQL database, and displayed together on a web dashboard. The system also generated an alert when an abnormal condition was detected. In five comparisons with reference instruments, the DHT22 showed average errors of 0.36% for temperature and 0.38% for humidity. The MobileNet model reached an aggregate validation accuracy of 91.3% on 1,200 labelled images. The average time from image capture to alert delivery was 4.7 seconds. These results show that environmental data and behavioural classification can be processed together on a Raspberry Pi-based platform under the tested conditions. The system was developed for quail monitoring, but its combination of sensors, real-time data, IoT communication, and image classification could also be used as a practical context for interdisciplinary learning.

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