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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advanced Science Computing and Engineering
Chamuthai, Likit
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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A Prototype Platform for Automated Chicken Feeding Control with an Embedded System Natho, Parinya; Chamuthai, Likit; Boonying, Sangtong; Tantidontanet, Nantiya
International Journal of Advanced Science Computing and Engineering Vol. 5 No. 1 (2023)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/ijasce.5.1.106

Abstract

In this paper present a prototype platform for automated chicken feeding control with an embedded system. This prototype is an automated real-time chicken feeding mixing system that will help chicken farmers become more automated, which will be highly beneficial for the expansion of the chicken poultry farm in Phra Nakhon Si Ayutthaya, Thailand. This machine may take the role of a person in the chicken-feeding process, solving the farm's labor shortage. The apparatus for feeding chickens makes use of an Arduino Uno board as the heart of an embedded system controller. The two major components of this device are an Arduino that controls the servomotor opening for the food container and another Arduino that controls the servomotor opening for the food ingredients in a mixed food container. The prototype platform will have an alarm when the food ingredients are at the minimum quantity. This also helps the chickens get the amount of food and nutrients that are appropriate for their age while reducing labour costs, saving food, and controlling chicken feeding on time.
An artificial intelligence technology for promoting hom-thong banana agriculture system Tanveenukool, Ratsames; Somsuphaprungyos, Suwit; Nokkurth, Boonyarit; Chamuthai, Likit; Bonguleaum, Patumwadee; Natho, Parinya
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp568-579

Abstract

The hom-thong banana, being a high-value Thai export variety, is facing significant risk from disease outbreaks affecting crop yield and quality. Traditional visual inspection methods in detection of diseases are labor consuming, error-prone. This research addresses these limitations by developing a new artificial intelligence (AI)-based automatic disease detection system for the hom-thong banana industry on top of cutting-edge computer vision technology. The study employed deep learning object detection models, contrasting Roboflow, you only look once (YOLO)v11, and YOLOv12 architectures, which were trained on a large dataset of 2,576 images of Thai banana plantations. With systematic data augmentation techniques, the dataset was augmented to 6,184 images of seven types of disease under varied environmental conditions. The method entailed extensive preprocessing and evaluation of performance through precision, recall, and mean average precision (mAP) metrics. Outcomes indicated that YOLOv12 outperformed with 93.3% accuracy, 83.3% sensitivity, and 86.3% mAP@50 compared to standard inspection schemes. This research is applicable to Thailand's smart agriculture initiative by providing farmers with low-cost, accurate, and effective disease monitoring equipment. The application of this AI system has the ability to enhance the yield of crops, reduce losses, and enhance the competitiveness of Thai banana exports in the global market, in support of sustainable agricultural development.
Co-Authors Bonguleaum, Patumwadee Boonying, Sangtong Natho, Parinya Nokkurth, Boonyarit Somsuphaprungyos, Suwit Tantidontanet, Nantiya Tanveenukool, Ratsames