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
คณะสถาปัตยกรรม ศิลปะและการออกแบบ
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วิทยาลัยนวัตกรรมการผลิตขั้นสูง
Smart Agriculture has rapidly developed in recent years, particularly with the integration of robotics and automation technologies to improve production efficiency and reduce costs, thereby enhancing the quality of current agricultural practices. A key innovation in this area is the rail-based robotic arm, designed to enhance work efficiency using a rail system with high precision and effectiveness. The application of this robotic arm covers various processes, such as planting, sorting, maintenance, harvesting, and resource management, allowing continuous operation and reducing human labor in repetitive and high-risk tasks. Studies have shown that the use of rail-based robotic arms in agriculture can significantly improve work efficiency, reduce production costs, and effectively mitigate environmental impact. By using robots in agricultural processes, it is possible to reduce contamination, lower the risk of crop damage, and make agriculture more sustainable. Additionally, it can increase accuracy in operations on limited spaces or farms with diverse crops. From these findings, it can be concluded that adopting rail-based robotic arm technology in agriculture not only enhances long-term production efficiency but also promotes sustainable agriculture and maximizes resource use, meeting future agricultural demands
คณะวิศวกรรมศาสตร์
This thesis presents the application of deep learning for object classification. The selected deep learning architectures studied include Convolutional Neural Networks (CNN) and ResNet18. It covers data preparation, feature extraction, parameter tuning for accuracy comparison, and performance evaluation of the selected models. The aim is to propose an efficient model for use in devices that assist visually impaired individuals in classifying indoor objects and providing sound alerts.