UAV-Based High-Throughput Phenotyping to Segment Individual Apple Tree Row Based on Geometrical Features of Poles and Colored Point Cloud
Bibliographic record
Abstract
Highlights Integrating RGB values and 3D coordinates of fruit trees provided phenotype-related data. The use of point clouds projection in tree rows segmentation has been confirmed. Using geometric features increased the accuracy of removing poles in an orchard. Segmenting fruit trees after removing poles on a single tree row was more accurate than traditional segmentation method. UAV equipped with LiDAR and a camera is promising for orchard high-throughput phenotyping. Abstract. High-throughput phenotyping (HTP) of fruit trees is important for providing crop geometrical information to evaluate their high yield genotypes. Unmanned aerial vehicle (UAV) is suitable for HTP by obtaining remote sensing data of large modern apple orchards, where each tree row needs to be segmented before segmenting a single tree. This study aims to develop a method for segmenting each row without noise (ERWON) of apple trees based on integrating RGB values and three-dimensional coordinates by UAV. A robust, real-time, RGB-colored, and LiDAR-inertial-visual tightly-coupled state estimation network was used to form a dense map of the orchard, which provided datasets of colored point clouds. Supporting poles were removed from the point clouds based on the consistent number of half upper parts and lower parts. Random sampling and an effective local feature aggregator were trained to segment ERWON after pole segmentation. Results showed that a precision of 0.971, a recall of 0.984, and an intersection-over-union of 0.817 for ERWON segmentation were achieved. This method proposed a potential solution for addressing the challenge of accurately and efficiently segmenting ERWON in large orchards. It is expected to be helpful for obtaining general parameters, such as geometric, morphological, and textural characteristics, as well as more specific parameters relevant to a particular phenotyping task. Keywords: Apple trees, Detection, Point cloud, RGB-colored, Segmentation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".