Crispy Rice-berry Snack is a product made from broken rice-berry rice that has been processed into a snack that is thin and crispy, bite-sized. Broken rice-berry rice is cooked, finely ground, and mixed with other ingredients to increase its nutritional value, such as adding plant seeds, adding plant protein nutrients, and then forming it into sheets using heat. The resulting product is a thin sheet, purple-brown in color, crispy, and has the smell of the ingredients used in the production process. It does not contain sugar or sweeteners. It is used as a snack with tea or coffee. Crispy Rice-berry Waffle is a product that contains complete nutrients, including carbohydrates, protein, and fat, which are derived from the ingredients in the production formula.
เป็นการเพิ่มมูลค่าใหักับข้าวหักไรซ์เบอร์ โดยนำมาแปรรูปเป็นผลิตภัณฑ์อาหารที่รับประทานได้ง่าย
คณะสถาปัตยกรรม ศิลปะและการออกแบบ
Design a graphic concept for a vending machine and its surrounding area (5x6 meters) featuring INGU skincare products
คณะเทคโนโลยีการเกษตร
Durian is a crucial economic crop of Thailand and one of the most exported agricultural products in the world. However, producing high-quality durian requires maintaining the health of durian trees, ensuring they remain strong and disease-free to optimize productivity and minimize potential damage to both the tree and its fruit. Among the various diseases affecting durian, foliar diseases are among the most common and rapidly spreading, directly impacting tree growth and fruit quality. Therefore, monitoring and controlling leaf diseases is essential for preserving durian quality. This study aims to apply image analysis technology combined with artificial intelligence (AI) to classify diseases in durian leaves, enabling farmers to diagnose diseases independently without relying on experts. The classification includes three categories: healthy leaves (H), leaves infected with anthracnose (A), and leaves affected by algal spot (S). To develop the classification model, convolutional neural network (CNN) algorithms—ResNet-50, GoogleNet, and AlexNet—were employed. Experimental results indicate that the classification accuracy of ResNet-50, GoogleNet, and AlexNet is 93.57%, 93.95%, and 68.69%, respectively.
คณะวิศวกรรมศาสตร์
This research focuses on the development of a 3D-printed spectrophotometer. The device was designed using AutoCAD, ensuring durability, low cost, and portability. It is intended for measuring the amount of formaldehyde in fresh seafood.