Computer Vision Based Autonomous Robotic System for 3D Plant Growth Measurement
Bibliographic record
Abstract
Research on increasing the production of crops is increasingly important these days. This research needs a way to quantitatively measure the 3D growth of plants under controlled environments to allow a cost versus benefits analysis. Plant scientists need a non-invasive, non-destructive method to quantitatively measure the 3D growth of plants. Traditional methods, for example, measuring weight, area or volume, often negatively affects the future plant growth. Also the manual nature of this measurement can be quite time consuming, tedious and error prone. Some recent effort have been reported in the literature about the construction of autonomous systems for plant phenotype, but these are not practical for large scale accurate 3D plant growth computation. To the best of our knowledge, we are the first in the world to attempt truly 3D approach via robot assisted plant growth analysis using 3D imaging and laser scanning technology. We describe an automated system to perform 3D plant modelling using a laser scanner mounted on a robot arm to capture 3D plant data. We present a detailed overview of the system integration, including the robotic arm, laser scanner and a programmable growth chamber. We also show some results on reconstructing the 3Dmodel of a growing plant which is better than the current state of the art.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".