Bibliographic record
Abstract
Developing machine vision algorithms that can correctly segment plant stems can significantly impact plant agriculture in greenhouses. If the stem can be accurately segmented, then other operations like growth monitoring, disease detection, and robotic de-leafing can be completed autonomously with minimal human intervention. However, segmenting plant stems in visual images of plants in greenhouses is a challenging task. Plants are highly occluded, overlapping, and grow in different directions. This thesis investigates this problem using three approaches. The first is based on modeling the plant as a non-rigid object: Active Shape Model (ASM). The second is based on machine learning: the Hough-forest technique. The last is based on using heuristic image processing algorithm: Vine search. The three approaches were tested on a benchmark dataset consisting of 100 images collected from commercial greenhouse operations in southern Ontario, Canada. Results show that while ASM handles deformable objects the best, it required a lot of processing time to achieve good results. Hough-forest outperformed other algorithms. It had the highest segmentation accuracy (99.6%), and it required the shortest processing time.
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".