IoT 기술과 머신러닝 기술을 융합한 효율적인 스마트 팜 프레임워크 설계

Recently, farmers are attempting various efforts related to smart farms to improve agricultural competitiveness such as labor, energy, and quantity so that crops can be grown efficiently using ICT technology. However, smart farms have the problem of not reflecting all external factors (weather, pests, sunlight, etc.) affecting farms in crop production. In this paper, we propose a smart farm framework that combines IoT technology and machine learning technology that can properly manage the growth environment information of crops produced by smart farms. The proposed framework has improved productivity, cost reduction, and labor cost savings by applying IoT technology and artificial intelligence technology to smart farms. The proposed framework finds and reflects environmental factors that may affect the growth and development of crops. In addition, after pre-extracting crop growth environment information into IoT data, the proposed framework applied principal component analysis (PCA) to capture feature information of nonlinear correlation between IoT data during IoT data selection. The proposed framework integrated and managed crop growth environment information into IoT data, enabling periodic automatic remote management according to the type and characteristics of IoT data. Since the proposed framework automatically manages crop growth environment information remotely, automatic remote environment management can be improved in terms of growth environment maintenance and environmental information monitoring compared to the existing environment.