This project presents an interactive kiosk system designed to facilitate students, staff, and visitors within the university campus. The kiosk provides real-time event updates, news, and university document access via QR codes or email. It integrates a 3D map of the engineering department with navigation assistance, allowing users to locate offices and other facilities efficiently. Additionally, it features a room booking system, enabling users to reserve spaces through an online platform and check in via QR code scanning at the kiosk. By integrating digital technology and smart urban solutions, this system enhances accessibility, campus management, and visitor experience.
As KMITL continues to grow with an increasing number of students, there is a greater need for efficient campus navigation and accessible information. In this new era of technology, it is essential to upgrade our systems to better facilitate the university community, starting with the RAI department. Many students, especially newcomers, struggle with locating buildings and facilities due to unfamiliar location names across the campus. This interactive kiosk serves as a one-stop service, providing students, staff, and visitors with essential university information, including admission details, without the need to search elsewhere. By integrating smart solutions, this project enhances accessibility, convenience, and overall campus experience.
คณะสถาปัตยกรรม ศิลปะและการออกแบบ
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คณะเทคโนโลยีการเกษตร
Durian is a crucial economic crop of Thailand and one of the most exported agricultural products in the world. However, producing high-quality durian requires maintaining the health of durian trees, ensuring they remain strong and disease-free to optimize productivity and minimize potential damage to both the tree and its fruit. Among the various diseases affecting durian, foliar diseases are among the most common and rapidly spreading, directly impacting tree growth and fruit quality. Therefore, monitoring and controlling leaf diseases is essential for preserving durian quality. This study aims to apply image analysis technology combined with artificial intelligence (AI) to classify diseases in durian leaves, enabling farmers to diagnose diseases independently without relying on experts. The classification includes three categories: healthy leaves (H), leaves infected with anthracnose (A), and leaves affected by algal spot (S). To develop the classification model, convolutional neural network (CNN) algorithms—ResNet-50, GoogleNet, and AlexNet—were employed. Experimental results indicate that the classification accuracy of ResNet-50, GoogleNet, and AlexNet is 93.57%, 93.95%, and 68.69%, respectively.
วิทยาเขตชุมพรเขตรอุดมศักดิ์
This project focuses on developing a work tracking system for team members. Python is used to extract data from Excel files and import it into a SQL Server database for systematic data management. The system includes a function to notify task status via LINE and displays reports via Power BI, allowing supervisors to track progress and evaluate team members' performance efficiently. Additionally, the system helps promote work and time management skills for team members.