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
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คณะเทคโนโลยีสารสนเทศ
Facial Expression Recognition (FER) has attracted considerable attention in fields such as healthcare, customer service, and behavior analysis. However, challenges remain in developing a robust system capable of adapting to various environments and dynamic situations. In this study, the researchers introduced an Ensemble Learning approach to merge outputs from multiple models trained in specific conditions, allowing the system to retain old information while efficiently learning new data. This technique is advantageous in terms of training time and resource usage, as it reduces the need to retrain a new model entirely when faced with new conditions. Instead, new specialized models can be added to the Ensemble system with minimal resource requirements. The study explores two main approaches to Ensemble Learning: averaging outputs from dedicated models trained under specific scenarios and using Mixture of Experts (MoE), a technique that combines multiple models each specialized in different situations. Experimental results showed that Mixture of Experts (MoE) performs more effectively than the Averaging Ensemble method for emotion classification in all scenarios. The MoE system achieved an average accuracy of 84.41% on the CK+ dataset, 54.20% on Oulu-CASIA, and 61.66% on RAVDESS, surpassing the 71.64%, 44.99%, and 57.60% achieved by Averaging Ensemble in these datasets, respectively. These results demonstrate MoE’s ability to accurately select the model specialized for each specific scenario, enhancing the system’s capacity to handle more complex environments.
คณะอุตสาหกรรมอาหาร
The Mahachanok mango sauce is crafted from low-grade mangoes sourced from Ban Nong Bua Chum in Kalasin Province. Utilizing advanced food science technology, it effectively reduces agricultural waste and enhances product quality. This sauce is enriched with prebiotic fiber that supports the growth of beneficial gut microorganisms. With low sugar content, it is a healthy choice free from artificial colors and flavors. Its rich, natural taste makes it versatile, perfect for enhancing a wide variety of dishes, both savory and sweet.