Smart Agriculture has rapidly developed in recent years, particularly with the integration of robotics and automation technologies to improve production efficiency and reduce costs, thereby enhancing the quality of current agricultural practices. A key innovation in this area is the rail-based robotic arm, designed to enhance work efficiency using a rail system with high precision and effectiveness. The application of this robotic arm covers various processes, such as planting, sorting, maintenance, harvesting, and resource management, allowing continuous operation and reducing human labor in repetitive and high-risk tasks. Studies have shown that the use of rail-based robotic arms in agriculture can significantly improve work efficiency, reduce production costs, and effectively mitigate environmental impact. By using robots in agricultural processes, it is possible to reduce contamination, lower the risk of crop damage, and make agriculture more sustainable. Additionally, it can increase accuracy in operations on limited spaces or farms with diverse crops. From these findings, it can be concluded that adopting rail-based robotic arm technology in agriculture not only enhances long-term production efficiency but also promotes sustainable agriculture and maximizes resource use, meeting future agricultural demands
ประเทศไทยเป็นประเทศเกษตรกรรม การที่จะนำเทคโนโลยีเข้ามาช่วยพัฒนาระบบการเกษตรกรรม ให้มีความทันสมัย มีประสิทธิภาพ เพื่อช่วยยกระดับอุตสาหกรรมการเกษตรของไทยนั้น เป็นสิ่งที่มีความสำคัญมาก เราจึงได้พัฒนา “เเขนกลระบบรางเพื่อการเกษตรอัฉริยะ” โดยเทคโนโลยีนี้ช่วยลดการใช้แรงงานคน เพิ่มความแม่นยำในการทำงาน และสามารถทำงานได้ตลอดเวลาไม่หยุดพัก นอกจากนี้ยังช่วยลดต้นทุนการผลิตและเพิ่มผลผลิต ทำให้เกษตรกรสามารถแข่งขันในตลาดโลกได้ดียิ่งขึ้น และช่วยพัฒนาความยั่งยืนในภาคการเกษตรของประเทศไทย
คณะทันตแพทยศาสตร์
Aggregatibacter actinomycetemcomitans is a key pathogen in periodontal disease, damaging periodontal ligaments and alveolar bone through biofilm formation. D-LL-31, an engineered antimicrobial peptide, exhibits superior biofilm-killing ability compared to conventional treatments, while DNase I enhances its efficacy by disrupting the biofilm matrix. This study evaluated the combined effects of D-LL-31 and DNase I on A. actinomycetemcomitans biofilms. Results showed that D-LL-31 effectively eradicated biofilms, and its combination with DNase I further enhanced biofilm disruption without cytotoxicity to gingival epithelial cells. The D-LL-31 and DNase I combination shows potential for development as a mouthwash to improve oral health and combat periodontal disease.
คณะวิทยาศาสตร์
Developing a Smart Farming Simulation Utilizing LoRa Communication and Presenting Knowledge on LoRa Communication System Components
คณะวิศวกรรมศาสตร์
This cooperative education project aims to enhance the efficiency of Hydrogen Manufacturing Unit 2 (HMU-2) and Pressure Swing Adsorption 3 (PSA-3) by using AVEVA Pro/II process modeling and a Machine Learning model for process simulation. The study found that the AVEVA Pro/II model predicted outcomes with deviations ranging from 0–35%, including a hydrogen flow rate deviation from the PSA unit of 12%, exceeding the company’s acceptable limit of 10%. To address this, a Machine Learning model based on the Random Forest algorithm was developed with hyperparameter tuning. The Machine Learning model demonstrated high accuracy, achieving Mean Squared Errors (MSE) of 8.48 and 0.18 for process and laboratory data, respectively, and R-squared values of 0.98 and 0.88 for the same datasets. It outperformed the AVEVA Pro/II model in predicting all variables and reduced the hydrogen flow rate deviation to 4.75% and 1.35% for production rates of 180 and 220 tons per day, respectively. Optimization using the model provided recommendations for process adjustments, increasing hydrogen production by 7.8 tons per day and generating an additional annual profit of 850,966.23 Baht.