This project aims to develop an AI-powered system for detecting and classifying wall cracks using image processing. It identifies different crack types, assesses severity, and ensures accuracy across various image conditions. The goal is to support preventive maintenance by enabling early detection of structural issues, reducing repair costs, and improving safety.
รอยร้าวบนผนังอาจส่งผลต่อ ความปลอดภัยของอาคาร แต่การตรวจสอบยังต้องพึ่ง ผู้เชี่ยวชาญ โครงงานนี้จึงพัฒนา ระบบ AI เพื่อตรวจจับและจำแนก รอยร้าวอัตโนมัติ ผ่าน การประมวลผลภาพ ช่วยให้ ประเมินเบื้องต้นได้ง่าย ลดค่าใช้จ่าย และส่งเสริม การบำรุงรักษาเชิงป้องกัน
คณะเทคโนโลยีการเกษตร
This project presents a design and management approach for agricultural land in Kanchanaburi Province. The case study area is situated in Wangdong Subdistrict, Mueang Kanchanaburi District, covering an area of approximately 18 rai (7.2 acres). As the user seeks a simplified lifestyle in the countryside, surrounded by nature, the design aligns with this vision of simplicity and sustainability. The land is systematically allocated to optimize the benefits for both daily living and agricultural industry development. The crop cultivation zones are designed to suit the local climate and plant varieties, ensuring high-quality yields for continuous utilization. Meanwhile, the livestock zones are clearly delineated to maintain balance and organization. This approach not only ensures food security and income generation but also promotes a lifestyle that harmonizes with nature, minimizes environmental impact, and supports the long-term development of an efficient and eco-friendly agricultural industry. Comprehensive attention is given to the positioning of various zones, considering wind direction and sunlight exposure. Additionally, the design undergoes a rigorous drafting and review process to ensure the optimal outcomes for the land's utilization.
วิทยาเขตชุมพรเขตรอุดมศักดิ์
Traditional Thong Yip dessert, or locally known as Khee Man dessert, is a dessert made from flour made from yellow rice.
วิทยาเขตชุมพรเขตรอุดมศักดิ์
This research focuses on the design and development of a prototype Artificial Intelligence of Things (AIoT) system for monitoring and controlling irrigation using weather information. The system consists of four main components: 1) Weather Station – This component includes various sensors such as air temperature, relative humidity, wind speed, and sunlight duration, among others, to collect real-time weather data. 2) Controller Unit – This unit is equipped with machine learning algorithms or models to estimate the reference evapotranspiration (ETo) and calculate the plant’s water requirement by integrating the crop coefficient (Kc) with other plant-related data. This enables the system to determine the optimal irrigation amount based on plant needs automatically. 3) User Interface (UI) and Display – This section allows farmers or users to input relevant information, such as plant type, soil type, irrigation system type, number of water emitters, planting distance, and growth stages. It also provides a display for monitoring and interaction with the system. 4) Irrigation Unit – This component is responsible for controlling the water supply and managing the irrigation emitters to ensure efficient water distribution based on the calculated requirements.