This research aims to develop an automatic gemstone color sorting machine to overcome the limitations of manual color sorting, which can be restricted by speed and accuracy. This study applies deep learning technology to analyze and classify gemstone colors precisely, developing an algorithm capable of accurately detecting and categorizing color shades. An automated conveyor system was also designed to efficiently transport gemstones through the color sorting process, allowing for continuous operation. The sorting machine works by capturing high-resolution images of the gemstones, processing them with software to classify color shades, and directing each gemstone to its designated position on the automated conveyor. Experimental results demonstrate that the automated color sorting machine, integrated with the conveyor system, achieves high speed and accuracy, significantly reducing labor costs and enhancing the efficiency of gemstone color sorting.
ปัจจุบันการตรวจสอบเฉดสีของพลอยถือเป็นขั้นตอนสำคัญในการประเมินคุณภาพ ทั้งในกระบวนการผลิตและการค้าขาย อย่างไรก็ตามการตรวจสอบเฉดสีที่ดำเนินการโดยมนุษย์นั้นมีข้อจำกัดหลายประการ ซึ่งหนึ่งในนั้นเป็นเรื่องความสามารถในการแยกเฉดสีที่ซับซ้อน ซึ่งอาจทำให้การตรวจสอบใช้เวลานาน และมีความแม่นยำต่ำ จากปัญหาดังกล่าว จึงได้พัฒนาแนวคิดในการสร้างเครื่องคัดแยกเฉดสีพลอยอัตโนมัติ โดยใช้ระบบ Computer Vision ร่วมกับระบบอัตโนมัติในการวิเคราะห์เฉดสีของพลอย เพื่อลดข้อจำกัดของการตรวจสอบด้วยมนุษย์ เพิ่มความแม่นยำและประสิทธิภาพในการทำงาน รวมถึงทำให้กระบวนการตรวจสอบเป็นไปอย่างรวดเร็วและมีมาตรฐานของเฉดสี GIA (Gemological Institute of America)
คณะวิศวกรรมศาสตร์
Jaundice, a common condition in infants that results from high bilirubin levels in the blood, often requires early diagnosis and monitoring to prevent severe complications, especially in newborns. Traditional diagnostic methods can be time-consuming and subject to human error. This study proposes an approach for real-time jaundice detection using advanced image processing techniques and machine learning algorithms. By analyzing images captured in RGB color spaces, pixel values are extracted and processed through Otsu’s thresholding and morphological operations to detect color patterns indicative of jaundice. A classifier model is then trained to distinguish between normal and jaundiced conditions, offering an automated, accurate, and efficient diagnostic tool. The system’s potential to operate in real-time makes it particularly suited for clinical settings, providing healthcare professionals with timely insights to improve patient outcomes. The proposed method represents a significant innovation in healthcare, combining artificial intelligence and medical imaging to enhance the early detection and management of jaundice, reducing reliance on manual interventions and improving overall healthcare delivery.
คณะเทคโนโลยีการเกษตร
Rice is a salt-sensitive crop. The objective of this study was to evaluate the effect of salinity at flowering stage on physiological traits and yield of landrace rice. The experiment design was 4*10 Factorial in RCBD with 4 replications. Factor A was four salinity levels: control, 6, 12 and 16 dS/m; Factor B was 10 rice varieties. Data were collected on physiological traits and grain yield. The results showed that increasing salinity level decreased rice yield. The highest yield reduction was found when the rice received salt stress at 16 dS/m. In addition, rice varieties showed different yield performance when exposed to salt stress. In this found that Hom Yai variety had the lowest yield reduction when grown at 16 dS/m salinity level and did not differ from salt tolerant check variety.
คณะวิทยาศาสตร์
Development of Hand Cream from Murraya Extract Using an Eco-Friendly Extraction Process. This research focuses on extracting active compounds from Murraya paniculata using a water-based, environmentally friendly method. The extract exhibits outstanding antibacterial properties and anti-oxidant. It is incorporated into a hand cream formulation.