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.
ผู้ใช้งานต้องการสร้างบ้านสวนเกษตรเพื่อใช้เป็นที่พักผ่อนอาศัยโดยมีพื้นที่สำหรับปลูกผักและผลไม้ รวมถึงโซนเลี้ยงสัตว์เพื่อสร้างรายได้จากการขาย ผลผลิตจากสวนและสัตว์เลี้ยงจะช่วยให้สามารถใช้ชีวิตอย่างยั่งยืนและพึ่งพาตนเองได้ รวมถึงเป็นการสร้างพื้นที่ที่ให้ความสงบและใกล้ชิดกับธรรมชาติ ซึ่งจะช่วยให้ผู้ใช้งานสามารถมีชีวิตที่สมดุล โดยไม่เพียงแต่เป็นสถานที่พักผ่อน แต่ยังเป็นแหล่งรายได้ที่มั่นคงในอนาคตด้วย
คณะวิศวกรรมศาสตร์
The evaluation of mango yield and consumer behavior reflects an increasing awareness of product origins, with a growing demand for traceability to understand how the produce has been cultivated and managed. This study explores the relationship between mango characteristics and cultivation practices before harvest, using location identification to provide insights into these processes. To achieve this, a model was developed to detect and locate mangoes using 2D images via a Deep Learning approach. The study also investigates techniques to determine the real-world coordinates of mangoes from 2D images. The YOLOv8 model was employed for object detection, integrated with camera calibration and triangulation techniques to estimate the 3D positions of detected mangoes. Experiments involved 125 trials with randomized mango positions and camera placements at varying yaw and pitch angles. Parameters extracted from sequential images were compared to derive the actual 3D positions of the mangoes. The YOLOv8 model demonstrated high performance with prediction metrics of Precision (0.928), Recall (0.901), mAP50 (0.965), mAP50-95 (0.785), and F1-Score (0.914). These results indicate sufficient accuracy for predicting mango positions, with an average positional error of approximately 38 centimeters.
คณะวิศวกรรมศาสตร์
This project develops a platform using computer vision to analyze real-time CCTV footage for detecting traffic law violations, such as crossing solid lines. The system can automatically identify and record traffic rule infringements, improving law enforcement efficiency and reducing the workload of traffic police officers. Moreover, it plays a crucial role in developing smart city systems by integrating data to enhance traffic management and road safety.
วิทยาเขตชุมพรเขตรอุดมศักดิ์
Bone tissue scaffolds are made from biomaterials that support rapid repair and healing. Scaffold fabricators have produced materials that are able to degrade a biosystem or human body excellently. Thus, this work aims to study the optimization of materials, shape, and the 3D printing process with FDM. Finite element analysis is also used to predict mechanical properties of the scaffold and find the optimal shape and pore size. However, the materials studied include PLA, PCL, and HA.