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.
To solve the problems of unclear boundaries and inconsistent influence weights among prioritization evaluation factors for grasping stacked fruit clusters by parallel robots, a fuzzy comprehensive evaluation method for the grasping prioritization of stacked fruit clusters based on a relative hierarchy factor set is proposed. According to the morphological features of stacked fruit clusters and motion features of parallel robots, a hierarchical tree model without a cross based on a subtree structure is constructed to analyze the multiple factors with unclear boundaries. A relative factor set with positive and negative effects is constructed, and a mathematical expectation is used to construct an average random consistency index and consistency satisfaction value for improving the consistency of influence weights and precision of consistency verification for a comparison matrix. The weight vector is constructed from the top to the bottom of the model, and the membership matrix of the multi−layer factors and grasping prioritization are calculated from bottom to top. The results showed that the average precision of grasping prioritization of stacked fruit clusters based on the proposed method increased by 27.77% compared with the existing fuzzy comprehensive evaluation method. The proposed method can effectively improve prioritization precision for grasping randomly stacked fruit clusters affected by multiple factors and can further realize accurate automatic sorting.
Background: Following the popularization of high-yielding rice in China, fast and efficient mechanized harvesting proved challenging. In addition, the physical characteristics of rice grains and stems are substantially affected during harvest by the field environment and harvest time. However, the combine harvester driver is focused on maximizing the outputs and does not consider the adverse effects of these factors during the rice harvest. Methods: We investigated the effects of the harvest time, spatial position, and temperature on the static friction coefficient of rice grains and stems of high-yielding rice using a field experiment. Results: The result difference in the static friction coefficient between the parallel and perpendicular placements of the rice stem on the steel plate was 9%, indicating that the contact configuration had a significant impact. The region, harvest time, and temperature significantly affected the static friction characteristics of the rice grains and stems. The most significant differences were observed in the X-direction. Conclusions: The optimum harvest time was 10:11 a.m.–3:30 p.m. and the optimum temperature was above 16.5 °C. A quantitative analysis of the effects of the harvest time and temperature on the static friction characteristics of rice provides reliable data for machine design optimization and standardization of harvests operations.
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