Object Detection in Tomato Greenhouses: A Study on Model Generalization
Bibliographic record
Abstract
Harvesting operations in agriculture are labour-intensive tasks. Automated solutions can help alleviate some of the pressure faced by rising costs and labour shortage. Yet, these solutions are often difficult and expensive to develop. To enable the use of harvesting robots, machine vision must be able to detect and localize target objects in a cluttered scene. In this work, we focus on a subset of harvesting operations, namely, tomato harvesting in greenhouses, and investigate the impact that variations in dataset size, data collection process and other environmental conditions may have on the generalization ability of a Mask-RCNN model in detecting two objects critical to the harvesting task: tomatoes and stems. Our results show that when detecting stems from a perpendicular perspective, models trained using data from the same perspective are similar to one that combines both perpendicular and angled data. We also show larger changes in detection performance across different dataset sizes when evaluating images collected from an angled camera perspective, and overall larger differences in performance when illumination is the primary source of variation in the data. These findings can be used to help practitioners prioritize data collection and evaluation efforts, and lead to larger-scale harvesting dataset construction efforts.
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 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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".