Evaluations of Effectiveness for Beverage Packaging Waste Sorting Using Huskylens Based on AI Machine Vision Suttipong Klongdee1 Songwut Boonsong2 Karin Kosiyanurak3 and Sommart Promput*,4
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บทคัดย่อ
Waste is a significant issue of which people worldwide should be aware, and excessive waste in Thailand continues to persist. This leads to environmental challenges. Thailand produces 27–28 million tons of solid waste per year. Therefore, waste separation is an important factor in mitigating these problems. Beverage packaging waste management has the opportunity to greatly reduce pollution in the environment. This research has developed a waste sorting machine that uses IoT technology, consisting of an Arduino Uno WiFi, Huskeylens, and other devices. The beverage packaging waste was categorized into three distinct groups: plastic bottles, glass bottles, and cans. There are a total of 30 samples. The findings demonstrate that the garbage sorter has the capacity to precisely identify the dataset. The Line application can efficiently and precisely inform consumers about the status of garbage pickup. The system's optimal operational distance is 30 cm, ensuring maximum efficiency between the Huskylens and the object at a luminance level of 305 lux. Furthermore, it can aid in the efficient categorization of recyclable waste and promote the reduction of environmental pollution. Furthermore, it can facilitate the effective classification of recyclable garbage and encourage the reduction of environmental contamination.
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ลิขสิทธิ์ต้นฉบับที่ได้รับการตีพิมพ์ในวารสารนวัตกรรมวิทยาศาสตร์เพื่อการพัฒนาอย่างยั่งยืนถือเป็นกรรมสิทธิ์ของคณะวิทยาศาสตร์และเทคโนโลยี มหาวิทยาลัยสวนดุสิต ห้ามผู้ใดนำข้อความทั้งหมดหรือบางส่วนไปพิมพ์ซ้ำ เว้นแต่จะได้รับอนุญาตอย่างเป็นลายลักษณ์อักษรจากคณะวิทยาศาสตร์และเทคโนโลยี มหาวิทยาลัยสวนดุสิต นอกจากนี้ เนื้อหาที่ปรากฎในบทความเป็นความรับผิดชอบของผู้เขียน ทั้งนี้ไม่รวมความผิดพลาดอันเกิดจากเทคนิคการพิมพ์
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