Plant scientists and breeders require high-quality phenotypic data. However, obtaining accurate manual measurements for large plant populations is often infeasible, due to the high labour requirement involved. This is especially the case for more complex plant traits, like the traits defining the plant architecture. Computer-vision methods can help in solving this bottleneck. The current work focusses on methods using 3D point cloud data to obtain phenotypic datasets of traits related to the plant architecture. A first step is the segmentation of the point clouds into plant organs. One of the issues in point-cloud segmentation is that not all plant parts are equally represented in the data and that the segmentation performance is typically lower for minority classes than for majority classes. To address this class-imbalance problem, we used a common practice to divide large point clouds into chunks that were independently segmented and recombined later. In our case, the chunks were created by selecting anchor points and combining those with points in their neighbourhood. As a baseline, the anchor points were selected in a class-independent way, representing the class distribution in the original data. Then, we propose a class-dependent sampling strategy to battle class imbalance. The difference in segmentation performance between the class-independent and the class-dependent training set was analysed first. Additionally, the effect of the number of points selected as the neighbourhood was investigated. Smaller neighbourhoods resulted in a higher level of class balance, but also in a loss of context that was contained in the points around the anchor point. The overall segmentation quality, measured as the mean intersection-over-union (IoU), increased from 0.94 to 0.96 when the class-dependent training set was used. The biggest class improvement was found for the "node," for which the percentage of correctly segmented points increased by 46.0 percentage points. The results of the second experiment clearly showed that higher levels of class balance did not necessarily lead to better segmentation performance. Instead, the optimal neighbourhood size differed per class. In conclusion, it was demonstrated that our class-dependent sampling strategy led to an improved point-cloud segmentation method for plant phenotyping.
Keywords: class imbalance; class-dependent sampling; deep learning; plant phenotyping; point-cloud segmentation.
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