The Kachatthai Project in Surin Province has been developed as a space to promote and generate income for farmers, incorporating a design concept that reflects the unique identity of Surin Province as its main guideline.
จังหวัดสุรินทร์เป็นพื้นที่ที่มีศักยภาพด้านการเกษตรและเป็นที่รู้จักในฐานะแหล่งเลี้ยงช้างที่สำคัญของประเทศไทย อีกทั้งยังมีอัตลักษณ์ทางวัฒนธรรมที่โดดเด่น โดยเฉพาะวิถีชีวิตของชาวพื้นเมืองและการเกษตรแบบดั้งเดิม อย่างไรก็ตาม เกษตรกรในพื้นที่ยังคงเผชิญกับปัญหาด้านรายได้และการตลาดของสินค้าเกษตร โครงการคชาทัยจึงถูกพัฒนาขึ้นเพื่อเป็นกลไกในการส่งเสริมและสนับสนุนเกษตรกร รวมถึงช่วยเพิ่มมูลค่าทางเศรษฐกิจและยกระดับคุณภาพชีวิตของประชาชนในท้องถิ่น
คณะวิศวกรรมศาสตร์
This Project has been undertaken to address the need for skill development and knowledge enhancement in pneumatic systems and automation control, which are crucial in today’s manufacturing industry. Pneumatic systems play a vital role in various production processes, including machine control, automated devices, and assembly lines. However, the Department of Measurement and Control Engineering currently lacks a laboratory dedicated to the study and experimentation of pneumatic systems due to the deterioration and lack of maintenance of the previously used equipment. This has resulted in students missing the opportunity to practice essential skills required in the industrial sector. The authors of this thesis recognize the necessity of reviving and developing a pneumatic laboratory that can effectively support teaching, learning, and research activities. This project focuses on studying and developing industrial robotic arm control systems and pneumatic systems, integrating modern technologies such as Programmable Logic Controllers (PLC) and AI Vision. These systems are intended to be applicable to real-world industrial contexts. The outcomes of this project are expected to not only enhance the understanding of relevant technologies but also aim to transform the laboratory into a vital learning hub for current and future students. Furthermore, this initiative seeks to improve the competitiveness of students in the job market and support the development of innovations in the manufacturing industry in the years to come.
คณะอุตสาหกรรมอาหาร
Spent hens are laying hens that are over 18 months to 2 years old and no longer productive. The texture of spent hen meat is significantly tougher compared to broiler chickens, capons, and native chickens. Therefore, to increase the value of spent hens, a study was conducted to modify the texture of the meat by restructuring it with carrageenan and tenderizing it by marinating it in bromelain solution at different concentrations. The experiment found that restructuring with carrageenan and using bromelain enzyme resulted in a newly formed product and significantly improved the tenderness of the meat compared to chicken meat that was not treated with carrageenan and bromelain enzyme.
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.