Abstract
Height is a key factor in monitoring the growth status and rate of crops. The point cloud generated by the Structure from Motion (SfM) algorithm based on Unmanned Aerial Vehicle (UAV) images can quickly estimate the crop height in the target area at a lower cost. However, crop leaves started to cover the ground gradually from the beginning of the stem elongation stage, making more and more ground points below the canopy disappear in the data. Therefore, the terrain undulations in the target area and outliers in the point cloud will seriously affect the height estimation accuracy. This paper proposed a new method to estimate the height of winter wheat based on UAV point cloud. Random Sample Consensus (RANSAC) was applied to obtain the ground points from the point cloud. Then, the missing ground points were fitted according to the known ground points. Our approach achieved crop height monitoring with an R2 of 0.76. Fitting the missing ground points simulated the terrain undulations effectively and improved the accuracy of estimated crop height.
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