The design site of 22 hectares of agricultural land in this area is located in Tatata District, Iglenburg Province. The design concept of this area aims to encourage farmers and communities to earn income from the agricultural products produced in this area, and emphasizes that this area is a "living market", which can not only create food from a variety of crops, but also establish a balanced ecosystem and promote biodiversity in this area. The market will become a sustainable market area, which is not only beneficial to society in promoting economy, but also beneficial to the environment. The area will serve as a social and learning center, where community members can fully exchange agricultural information and experience and stimulate the economy in the community in another way.
เพื่อศึกษาการออกแบบภูมิทัศน์
คณะวิศวกรรมศาสตร์
This capstone project develops an AI-powered chatbot to address cybersecurity vulnerabilities, leveraging the Common Vulnerabilities and Exposures (CVE) system and the Common Vulnerability Scoring System (CVSS). The chatbot will provide accessible and informative support for understanding and mitigating these vulnerabilities, potentially leading to significant improvements in cybersecurity practices.
คณะสถาปัตยกรรม ศิลปะและการออกแบบ
Here, We Luckier in Love Everyday". Introducing you a Lakshmi 2025 Edition. Amidst the buzz of the mall, take charge of your love destiny—because fate is so last season.
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.