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Wellness center Project

Abstract

Thammadul Wellness Center is a health and wellness center focused on restoring balance to the body and mind through natural therapy and holistic care. Designed as a retreat for relaxation and rejuvenation, the center integrates alternative medicine, nutritional therapy, appropriate exercise, and an environment that promotes tranquility. The center offers a wide range of services, including Thai herbal spa treatments, yoga and meditation, nutritional counseling, and personalized health restoration programs. The architectural design emphasizes the use of natural materials and a setting that harmonizes with the surrounding environment, creating a serene atmosphere that allows visitors to reconnect with nature. Thammadul Wellness Center aims to promote the concept of holistic health care, emphasizing prevention rather than treatment, so that guests can adopt these wellness practices into their daily lives sustainably.

Objective

1.ความเครียดและปัญหาสุขภาพจากวิถีชีวิตสมัยใหม่ 2.อนุรักษ์และส่งเสริมภูมิปัญญาท้องถิ่นด้านสุขภาพ 3.ให้ความสำคัญกับการดูแลสุขภาพเชิงป้องกันมากขึ้น ทำให้แนวทางธรรมชาติบำบัดได้รับความนิยม

Other Innovations

The extraction of prebiotic from spent coffee grounds

คณะอุตสาหกรรมอาหาร

The extraction of prebiotic from spent coffee grounds

Spent coffee grounds (SCG) are a byproduct of the coffee brewing process, and their quantity continues to increase due to the growing global coffee consumption. SCG contain beneficial compounds such as polysaccharides, dietary fibers, and antioxidants, which can be utilized in various applications, including prebiotic extraction. This study focuses on extracting prebiotics from SCG using acid hydrolysis and enzymatic hydrolysis methods to evaluate their potential in promoting the growth of beneficial gut microorganisms. The expected results of this research include adding value to coffee industry waste, reducing organic waste, and providing a sustainable approach to developing prebiotic products for use in the food and health industries. Furthermore, this study aligns with sustainable resource utilization and environmentally friendly practices.

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Crispy Rice Berry Waffle

คณะครุศาสตร์อุตสาหกรรมและเทคโนโลยี

Crispy Rice Berry Waffle

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.

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Detection of Durian Leaf Diseases Using Image Analysis and Artificial Intelligence

คณะเทคโนโลยีการเกษตร

Detection of Durian Leaf Diseases Using Image Analysis and Artificial Intelligence

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.

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