Leaves are used extensively as an indicator in research on tree growth. Leaf area, as one of the most important index in leaf morphology, is also a comprehensive growth index for evaluating the effects of environmental factors. When scanning tree surfaces using a 3D laser scanner, the scanned point cloud data usually contain many outliers and noise. These outliers can be clusters or sparse points, whereas the noise is usually non-isolated but exhibits different attributes from valid points. In this study, a 3D point cloud filtering method for leaves based on manifold distance and normal estimation is proposed. First, leaf was extracted from the tree point cloud and initial clustering was performed as the preprocessing step. Second, outlier clusters filtering and outlier points filtering were successively performed using a manifold distance and truncation method. Third, noise points in each cluster were filtered based on the local surface normal estimation. The 3D reconstruction results of leaves after applying the proposed filtering method prove that this method outperforms other classic filtering methods. Comparisons of leaf areas with real values and area assessments of the mean absolute error (MAE) and mean absolute error percent (MAE%) for leaves in different levels were also conducted. The root mean square error (RMSE) for leaf area was 2.49 cm². The MAE values for small leaves, medium leaves and large leaves were 0.92 cm², 1.05 cm² and 3.39 cm², respectively, with corresponding MAE% values of 10.63, 4.83 and 3.8. These results demonstrate that the method proposed can be used to filter outliers and noise for 3D point clouds of leaves and improve 3D leaf visualization authenticity and leaf area measurement accuracy.

The ability to detect and follow a specific person is considered to be a key prerequisite of service mobile robots in indoor environments. A novel vision-based tracking and following method for a mobile robot is proposed, which combines the clothes color features of the person's upper body and the contour of the head-shoulder. Horizontal-Projecting Probability Histogram (HPPH) of torso based on particular color clothes of upper body is proposed for extracting the pattern features, and the contour matching method by the head-shoulder is integrated to reduce the false positive detection rate, which significantly improves the tracking reliability. The proportional compensation method is utilized to enable the robot to follow the targeted person. Experimental results validate the robust performance of the approach.

Phenotyping of poplar seedling leaves based on a 3D visualization method

Automatic detection of pecan fruits based on Faster RCNN with FPN in orchard

English intelligent teaching has become a significant innovation in the field of education through the utilization of artificial intelligence (AI) technology. This paper proposes a comprehensive algorithmic framework aimed at automating the assessment of students' English writing and providing them with targeted feedback and suggestions. This framework integrates technologies such as natural language processing (NLP), machine learning, and integer linear programming (ILP), enabling teachers and students to enhance their English writing skills more effectively.