Agastache rugosa, a member of the Lamiaceae family, is valued for its essential oils and bioactive compounds, including tilianin, acacetin, and rosmarinic acid, known for their therapeutic effects. To meet the growing demand for high-quality plant products, we investigated cultivation techniques using stress treatments to enhance growth and bioactive compound production.
This study aims to develop recycling strategies for byproducts generated in indoor farming, addressing the need for sustainable solutions amid expanding production areas and regulatory gaps. By identifying and classifying key byproducts such as coir, rockwool, plant residues, and waste nutrient solutions, the research seeks to propose practical recycling policies through collaboration with institutions, businesses, and stakeholders.
This research focuses on developing core digital twin technologies for functional crops, enabling efficient cultivation and management of high-value-added plants. Key objectives include predictive modeling using AI, standardizing functional substances, and optimizing cultivation protocols. Through integrated environmental controls and field validations, the project aims to establish a futuristic smart farm model for stable and efficient production of functional crops.
Building-integrated rooftop greenhouse technology, which combines machinery, energy, architecture, and agricultural technology, not only promotes urban agriculture but also reduces building energy consumption, representing a true convergence technology for the future.
Globally, the therapeutic benefits of hemp(medical cannabis) are increasingly recognized, leading to the relaxation of legal restrictions on its use. However, the primary active compounds in hemp, cannabinoids, are highly influenced by cultivation conditions, often resulting in challenges for consistent production. Therefore, developing optimal environmental management technologies to ensure the stable production and high quality of hemp is crucial. This effort is essential for achieving sustainability in hemp cultivation and meeting the demands of the medical industry.
Plant factory systems that use artificial light as the primary light source differ significantly from natural sunlight conditions, leading to differences in morphogenesis and internal metabolites compared to plants grown under natural conditions. The aim is to develop an AI tool capable of controlling optimal light intensity and light quality based on the current growth state of plants, with a pattern similar to natural sunlight.
This project aims to improve agricultural efficiency for hemp cultivation by developing big data and AI-based predictive technologies for smart farms. It optimizes planning, production, and distribution through lifecycle prediction systems and a cultivation consulting platform. Key outcomes include comprehensive datasets, accurate prediction models, and essential management tools for hemp farming. By leveraging high-performance computing and innovative algorithms, the project enhances predictive capabilities, contributing to more efficient and sustainable hemp farming practices.
Due to the climate changes, the drought and flood often happen in crop growing period causing crop yield decrease severely. And rice demand also decreases because of changes of people’s dietary habit in Korea(the Statistics Korea). So developing a new culture technique ables to ensure crop yield and quality, meanwhile, apply to paddy field and upland is very important
Smart agricultural water management is crucial for sustainable farming and carbon neutrality in South Korea, where agriculture accounts for over half of the nation's water usage. With an aging farming population, attracting young farmers and modernizing agricultural practices through digital transformation is vital. This research focuses on developing technologies to optimize water use and tackle climate challenges.