RGB-Based Crop Coverage Estimation under Side-View and Top-View Imaging Conditions Using Deep Learning Crop coverage estimation is an important task in precision agriculture because it provides useful information about crop establishment, vegetation density, field uniformity, and growth development. Accurate estimation of canopy cover can support agronomic decision-making and help evaluate crop conditions in a more objective and consistent way. Although multispectral and hyperspectral imaging systems are commonly used for vegetation monitoring, RGB cameras offer a lower cost and more practical alternative for field applications. However, estimating crop coverage from RGB images remains challenging, especially when images are captured from a side-view perspective.