REMOTE SENSING TO UAV-BASED DIGITAL FARMLAND

This study presents preliminary observations of the first year of a crop monitoring experiment occurred in two soybean agriculture fields in the Arkansas delta. The project focuses on developing image processing and data integration techniques for UAV-based images to optimize advanced farm management such as soil microbial amendments. In particular, we present an effective algorithm that can use high-resolution UAV images efficiently to estimate sprout density and plant vigor/health throughout the growing season. Such plant characterization is extremely important for the identification of anomalous areas and provide easily interpretable information for a better decision making. We also present an integrative analysis of UAV-data with geophysical data and harvesting data, which shows high correlation between persistent spatial pattern of soil, plant phenology/growth, and crop yield.