This project aims to design and develop a propulsion system for agricultural equipment using RFID technology and evaluate its movement performance on different surfaces, including concrete and grass. The experiment focuses on examining the tag detection range under transmission power levels of 20 dBm, 23 dBm, and 26 dBm, as well as the impact of antenna angles on detection efficiency. Additionally, the system was tested in three movement scenarios: straight path, left turn, and right turn, at distances of 2 meters, 4 meters, and 6 meters. The results indicate that the system achieved the highest average speed of 0.4736 m/s and an average turning angle of 91.6° when moving in a straight path on a concrete surface at a distance of 4 meters. On a grass surface at the same distance, the average speed was 0.4483 m/s, with an average turning angle of 91.1°. For left and right turns, the movement on the concrete surface generally exhibited a higher average speed than on grass, particularly at a distance of 4 meters, where differences in turning angles were observed. This study provides insights into the factors affecting the movement of agricultural mowing equipment and serves as a foundation for enhancing the efficiency of propulsion systems in future developments.
ปัจจุบัน เทคโนโลยี RFID (Radio Frequency Identification) ได้รับการใช้งานอย่างแพร่หลายในหลากหลายอุตสาหกรรมและแอปพลิเคชันที่เติบโตอย่างรวดเร็ว เทคโนโลยีนี้ใช้คลื่นวิทยุในการระบุวัตถุที่ติดแท็กโดยไม่จำเป็นต้องมองเห็นหรือสัมผัสโดยตรง ทำให้การติดตามและระบุตำแหน่งมีความแม่นยำ คุ้มค่าทางเศรษฐกิจ และสามารถทำงานได้อย่างมีประสิทธิภาพในสภาพแวดล้อมที่หลากหลาย เช่น ภาคอุตสาหกรรมและการเกษตร ในภาคการเกษตร การใช้ RFID มีข้อดีหลายประการ เช่น ความสามารถในการทำงานกลางแจ้งโดยไม่ต้องพึ่งพาเซ็นเซอร์ราคาแพงหรือระบบสะท้อนสัญญาณที่ซับซ้อน ซึ่งช่วยลดต้นทุนและเพิ่มความแม่นยำในการควบคุมอุปกรณ์ทางการเกษตร งานวิจัยนี้มุ่งเน้น การออกแบบและพัฒนาระบบขับเคลื่อนต่อพ่วงอุปกรณ์ทางการเกษตรโดยใช้เทคโนโลยี RFID เพื่อศึกษาการเคลื่อนที่ของระบบบนพื้นผิวที่แตกต่างกัน ได้แก่ พื้นปูนคอนกรีตและสนามหญ้า โดยมีการติดตั้งแท็ก RFID บนเสาของแต่ละแถวเพื่อช่วยระบุตำแหน่งและทิศทางการเคลื่อนที่ของระบบ ผลการศึกษานี้จะช่วยให้เข้าใจข้อดีและข้อจำกัดของการใช้ RFID ในภาคการเกษตร ซึ่งสามารถนำไปต่อยอดเพื่อพัฒนาเทคโนโลยีการเกษตรให้มีประสิทธิภาพและลดต้นทุนในอนาคต
คณะอุตสาหกรรมอาหาร
A new jelly snack alternative for health-conscious individuals—delicious, convenient, and gut-friendly. Rich in probiotics and prebiotics, packed with antioxidants, and essential vitamins. Suitable for health enthusiasts and lactose-intolerant individuals. Free from artificial colors and flavors
คณะเทคโนโลยีการเกษตร
The extreme weathers according to PM 2.5 is a global problem with out any borders. This pollutant can directly attack human health. The objective of the study was aimed to develop medicinal plant essential oil emulsions in order to use to decrease PM 2.5 based on chemical characterization of water-soluble anions and cations. A mount of 31 medicinal plant essential oil emulsions were prepared and then initially careened and tested for their efficiency in reducing PM 2.5 under test chamber by spraying method. It was found that spraying for 1 hr with kaffir lime essential oil emulsion at 0.025% concentration could reduce PM 2.5 obtained from engine exhaust pipe effectively when PM 2.5 of 24.7 µg/m3 was detected within 6 hrs, followed by kaffir lime essential oil emulsion at 0.05% and Eucalyptus essential oil emulsion at 0.05% and 0.025% concentration resulting in 27.3, 30.0 and 95.3 µg/m3, respectively. Whereas, water (blank) and control group (water and carboxymethylcellulose, CMC 0.2%) showed high revels of PM 2.5 with 126.4 and 157.3 µg/m3, respectively. This kaffir lime essential oil emulsion at 0.025% concentration showed 3-6 time decline of PM 2.5 upward 2 hrs compared with control group. Field experiment was performed at 3 Bangkok parks, namely, Suantaweewanarom, Suanbankharepirom and Suanthonbureerom. There were many factors affecting the decline of PM 2.5 caused by this essential oil emulsion, particularly, the windy as well as temperature and humidity. PM 2.5 level tended to be decreased after the beginning of spraying. In general, PM 2.5 levels appeared at those 3 parks were decreased rapidly within 1 hr as by average of 21.8 (7.7-27.3) µg/m3, Whereas, decline of only 6.4 (5.0-8.0) µg/m3 was observed in control (water). Incase of calm wind, (10-20 km/hr) this plant essential oil emulsion could even reduce PM 2.5 at 37.0-44.0 µg/m3 and reached to 13.5-16.5 µg/m3 within 3 hrs. As high level of PM 2.5 as 98.0-101.0 µg/m3 , it could reduce PM 2.5 to be an average of 23.0-26.5 µg/m3 within 3 hrs, Whereas, the use of water performed low capacity of PM 2.5 reduction found with only 31.0-40.0 µg/m3. However, windy condition (15-35 km/hr), the efficacy of this essential oil emulsion seem to be lower but tended to work better than using water alone
คณะวิศวกรรมศาสตร์
This research suggested natural hemp fiber-reinforced ropes (FRR) polymer usage to reinforce recycled aggregate square concrete columns that contain fired-clay solid brick aggregates in order to reduce the high costs associated with synthetic fiber-reinforced polymers (FRPs). A total of 24 square columns of concrete were fabricated to conduct this study. The samples were tested under a monotonic axial compression load. The variables of interest were the strength of unconfined concrete and the number of FRRlayers. According to the results, the strengthened specimens demonstrated an increased compressive strength and ductility. Notably, the specimens with the smallest unconfined strength demonstrated the largest improvement in compressive strength and ductility. Particularly, the compressive strength and strain were enhanced by up to 181% and 564%, respectively. In order to predict the ultimate confined compressive stress and strain, this study investigated a number of analytical stress–strain models. A comparison of experimental and theoretical findings deduced that only a limited number of strength models resulted in close predictions, whereas an even larger scatter was observed for strain prediction. Machine learning was employed by using neural networks to predict the compressive strength. A dataset comprising 142 specimens strengthened with hemp FRP was extracted from the literature. The neural network was trained on the extracted dataset, and its performance was evaluated for the experimental results of this study, which demonstrated a close agreement.