Artificial intelligence for agriculture and environment is a collection of significant models for enviromental friendly Thailand development. The models create with machine learning and deep learning by Near infrared spectroscopy research center for agricultural and food products, including: Determining the nutrient needs (N P K) of durian trees by measuring durian leaves using a non-destructive technique using artificial intelligence, Identification of combustion properties of biomass from fast-growing trees and agricultural residues using non-destructive techniques combined with artificial intelligence, and Evaluation of global warming due to biomass combustion using non-destructive techniques using artificial intelligence. The basic technology used is Near infrared Fourier transform spectroscopy technology which measurement and output display can be done quickly without chemical, no requirement for special expert, and measurement price per sample is very low. But the instrument cannot be produced in Thailand.
ความเจริญก้าวหน้าทางวิชาการจะเป็นจริงเมื่อผลงานทางวิชาการถูกนำไปใช้ได้จริงในระบบการผลิตซึ่งมีนัยสำคัญทางเศรษฐกิจของประเทศ จึงได้นำผลงานของนักศึกษาและคณาจารย์ของศูนย์วิจัยเนียร์อินฟราเรดสเปกโทรสโกปีสำหรับผลิตผลเกษตรและอาหาร มานำเสนอในงานจัดแสดงนวัตกรรม KMITL Innovation Expo 2025 ในวันพฤหัสบดี 6 ถึง วันเสาร์ 8 มีนาคม 2568 ณ หอประชุมเจ้าพระยาสุรวงษ์ไวยวัฒน์ (วร บุนนาค) สจล. ซึ่งเป็นโอกาสดีของศูนย์วิจัยเนียร์อินฟราเรดสเปกโทรสโกปีสำหรับผลิตผลเกษตรและอาหารในการเปิดเผยผลงานทางวิชาการสำคัญซึ่งมีแนวโน้มสามารถนำไปใช้ได้จริงต่อสังคมเกษตรกรรมและสิ่งแวดล้อมเพื่อการพัฒนาประเทศ
คณะอุตสาหกรรมอาหาร
This study aimed to develop a formula and production process for snacks made from germinated brown rice flour and banana flour using the extrusion process. The results indicated that both germinated brown rice flour and banana flour could be effectively used as the main raw materials for snack production via extrusion. The proportion of flour in the formula and production conditions, such as moisture content of the raw materials, barrel temperature, and screw speed, significantly influenced the nutritional value, bioactive compound levels, and antioxidant activity of the final products.
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
วิทยาลัยการจัดการนวัตกรรมและอุตสาหกรรม
Air Rack is a product designed to address businesses with limited space and budget constraints for server rooms, cooling systems, and noise management. This system enables efficient use of IT equipment in open spaces, supporting both On-premise and On-cloud operations. It converts sensor data into digital information and displays it via a Dashboard, allowing users to monitor, analyze, and control the system remotely. Additionally, Air Rack significantly reduces power consumption and the costs associated with traditional server room management.