In a world increasingly focused on sustainability and reducing environmental impact, DreamHigh is pioneering an innovative approach to packaging solutions using mycelium—a natural, biodegradable, and renewable material derived from fungi. Our mission is to revolutionize the packaging industry by offering eco-friendly alternatives that not only reduce waste but also align with global efforts to combat climate change. Mycelium packaging offers a compelling alternative to traditional plastic and Styrofoam packaging, which contribute significantly to environmental pollution. It is fully biodegradable, compostable, and capable of breaking down in natural environments within weeks, leaving no toxic residues behind. Additionally, mycelium-based products are lightweight, durable, and customizable, making them suitable for a wide range of applications, from consumer goods packaging to protective shipping materials. DreamHigh’s business plan outlines a scalable production process leveraging advanced mycelium cultivation techniques and partnerships with local agricultural sectors to utilize agricultural waste as a key raw material. This not only ensures cost-efficiency but also supports a circular economy by repurposing waste that would otherwise be discarded.
เนื่องจากเราเล็งเห็นถึงปัญหาของการทิ้งโฟมหรือพลาสติกกันกระแทก ที่ใช้เวลาในการย่อยสลายนาน เราจึงนำตัวไมซีเลียมที่ใช้เวลาย่อยสลายไม่นานอีกทั้งยังเป็นมิตรต่อธรรมชาติ
คณะวิทยาศาสตร์
This study aimed to investigate the effectiveness of extracts from moringa seeds, roselle seeds, and tamarind seeds as coagulants to improve water quality in surface water sources. Extracts from these seeds serve as environmentally friendly coagulants and provide alternative options for enhancing surface water quality. The turbidity of surface water sources ranged between 14 and 24 NTU. The coagulation process used the Jar Test method, where the moringa seed, roselle seed, and tamarind seed extracts functioned as both primary coagulants and coagulant aids. In the preparation process, the seeds were finely ground and extracted using a 0.5-M sodium chloride (NaCl) solution. These extracts were then applied as coagulants to reduce turbidity and enhance water quality, with each concentration tested in 300 ml of water. The results indicated that the most effective way to remove turbidity using 2,000 mg/L of moringa seed extract, achieving a turbidity reduction of approximately 73.19% at a cost of 0.0309 baht per 300 ml of water. Followed by Tamarind seed extract, with a concentration of 4,000 mg/L, followed with a turbidity reduction of approximately 56.75% at a cost of 0.0933 baht per 300 ml. Lastly, roselle seed extract at 6,000 mg/L achieved a turbidity reduction of approximately 32.67% at a cost of 0.0567 baht per 300 ml of water.
คณะเทคโนโลยีการเกษตร
The innovation of the vertical aquaponics system for rearing golden apple snails integrating with vegetable cultivation by using substrates to water treatment. The system aims to maximize the use of vertical space, save water, and produce safe vegetables for consumption or commercial purposes, and to support living things. The golden apple snail excretes wastes/leftover food scraps that are filtered on the substrates used for water treatment. Meanwhile, natural bacteria help change these wastes into nutrients that plants can use. Therefore, the system is environmentally friendly.
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.