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
คณะวิศวกรรมศาสตร์
Given the fact that the equity market contributes a significant amount to Thai economy and increasing participants and interest by Thai companies, these facts inspire and motivate us to establish a study to analyze whether the stock market can indeed be an active booster of company performances and characteristics of companies which will be beneficial from being in the stock market. These results can support higher listing interest from companies, provide actionable ideas to companies aiming to improve their performance in the competitive arena, and suggest improvements for the stock market to further establish a stronger capital market penetration and foundation in Thailand. The main hypothesis driving this project is to examine whether “aging in the market” contributes to measurable improvements in a company’s performance. Specifically, we seek to understand if the presence of Thai companies in the Stock Exchange of Thailand correlates with enhanced operational outcomes, thereby providing insights into the true benefits of public listing on long-term performance.
คณะวิทยาศาสตร์
This work presents the fabrication of the handheld meter for potentiometric detection of Hg (II). The meter was constructed based on using an ion-sensitive field-effect transistor (ISFET) platform. The developed meter provides high accuracy and precision (%Recovery was in the range of 92.55 - 109.32 and %RSD was 2.38). It was applied to the analysis of cosmetic samples. The results by the developed electrode were not significantly different at a 95% confidence level compared to the results by using ICP-OES.
คณะเทคโนโลยีสารสนเทศ
This research presents a deep learning method for generating automatic captions from the segmentation of car part damage. It analyzes car images using a Unified Framework to accurately and quickly identify and describe the damage. The development is based on the research "GRiT: A Generative Region-to-text Transformer for Object Understanding," which has been adapted for car image analysis. The improvement aims to make the model generate precise descriptions for different areas of the car, from damaged parts to identifying various components. The researchers focuses on developing deep learning techniques for automatic caption generation and damage segmentation in car damage analysis. The aim is to enable precise identification and description of damages on vehicles, there by increasing speed and reducing the work load of experts in damage assessment. Traditionally, damage assessment relies solely on expert evaluations, which are costly and time-consuming. To address this issue, we propose utilizing data generation for training, automatic caption creation, and damage segmentation using an integrated framework. The researchers created a new dataset from CarDD, which is specifically designed for cardamage detection. This dataset includes labeled damages on vehicles, and the researchers have used it to feed into models for segmenting car parts and accurately labeling each part and damage category. Preliminary results from the model demonstrate its capability in automatic caption generation and damage segmentation for car damage analysis to be satisfactory. With these results, the model serves as an essential foundation for future development. This advancement aims not only to enhance performance in damage segmentation and caption generation but also to improve the model’s adaptability to a diversity of damages occurring on various surfaces and parts of vehicles. This will allow the system to be applied more broadly to different vehicle types and conditions of damage inthe future