ScholarWorks@동국대학교

초록

From 2020 to 2021, crop production increased by 54% globally, and the popularity of commercial agriculture to increase profitability is gradually increasing. However, global warming and climate issues make it difficult to maintain stable crop production. To improve crop production efficiency, techniques for efficiently managing large-scale commercial farmland are needed. This study proposes a satellite image-based soil moisture and onion yield prediction technique as a methodology for managing large-scale farmland. This preemptive soil moisture management technique effectively manages increased soil pressure, resulting in soil drying due to rising temperatures. To remotely identify agricultural land, vegetation indices were extracted from satellite image data, and K-means clustering was applied. Ensemble machine learning is performed on soil images collected from satellite images. This model combines soil physical properties with soil environmental factor information to develop a model. The results show that soil color information obtained from satellite images is highly correlated with soil organic matter content. The proposed model is validated using crop yield data and environmental factor data obtained from actual crop production experiments. Consequently, the proposed methodology can be effectively applied to manage large-scale farmland and enables decision-making to improve profitability.

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