UAV-mounted multispectral sensors are widely used to study crop health. Utilising the same cameras to capture close-up images of crops can significantly improve crop health evaluations through multispectral technology. Unlike RGB cameras that only detect visible light, these sensors can identify additional spectral bands in the red-edge and near-infrared (NIR) ranges. This enables early detection of diseases, pests, and deficiencies through the calculation of various spectral indices. In this work, the ability to use UAV-multispectral sensors for close-proximity imaging of crops was studied. Images of plants were taken with a Micasense Rededge-MX from top and side views at a distance of 1 m. The camera has five sensors that independently capture blue, green, red, red-edge, and NIR light. The slight misalignment of these sensors results in a shift in the swath. This shift needs to be corrected to create a proper layer stack that could allow further processing. This research utilised the Oriented FAST and Rotated BRIEF (ORB) method to detect features in each image. Random sample consensus (RANSAC) was used for feature matching to find similar features in the slave images compared to the master image (indicated by the green band). Utilising homography to warp the slave images ensures their perfect alignment with the master image. After alignment, the images were stacked, and the alignment accuracy was visually checked using true colour composites. The side-view images of the plants were perfectly aligned, while the top-view images showed errors, particularly in the pixels far from the centre. This study demonstrates that UAV-mounted multispectral sensors can capture images of plants effectively, provided the plant is centred in the frame and occupies a smaller area within the image.