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Estimation of rice plant height distribution plays a significant role in keeping the feed rate of rice combine harvesters stable. This is an effective way to guarantee the working stability of the whole machine, as a consequence, improving threshing and cleaning efficiency and reducing loss and damage rates. However, dense growth and leafy and bent branches of mature rice make it difficult to detect the lowest point of aggregated growing plants in three dimensional (3D) point cloud data. Therefore, the objective of this study was to put forward a method to estimate plant height distribution on the basis of a moving surface and 3D point cloud elevation. The statistical outlier removal (SOR) algorithm was used to reduce noise points far away from target point cloud body, and then moving surface fitting elevation was applied to achieve accurate classification of ground and crop point cloud data for plant height estimation. Experiments showed that, compared with the actual value, the average square root error (RMSE) of the estimation results was 8.29, the average absolute percentage error (MAPE) was 9.28%, and the average accuracy was 90%. The proposed method could accurately estimate the height of mature rice and is beneficial to calculating the feed rate in advance, which can provide a reference for further investigation in automatic and intelligent harvesting.
Sun et al. (Tue,) studied this question.