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
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
In Thailand, the quantity of old tires has been increasing annually, posing a significant environmental challenge due to their non-biodegradable material. However, old tires contain an internal porous structure, which suggests their potential application as sound-absorbing materials. Porosity is a key characteristic that enables materials to trap sound waves, making them effective for noise reduction. Therefore, this study aims to investigate and develop sound-absorbing materials from old tire rubber powder. The methodology involved mixing old tire powder with fresh latex at a ratio of 1:2, followed by drying at a temperature of 120°C for four hours. Subsequently, the physical properties influencing sound absorption, including density, porosity, and water absorption, were analyzed. The results indicated that the sound-absorbing material produced from old tire rubber powder showed a density of 0.96 g/cm³, a porosity value of 0.45, and a water absorption of 11.03%. Therefore, the findings suggest that old tire rubber powder has the potential to be effectively utilized as a sound-absorbing material.
คณะวิศวกรรมศาสตร์
This project focuses on the development of an automatic license plate recognition system that supports both standard and special license plates in Thailand. By utilizing Machine Learning technology, the system enhances the efficiency of license plate reading. It can process data from both images and videos. Users can register and subscribe to the service, allowing them to send data for processing through RESTful API, WebSocket, and registered IP cameras.