COMPUTER VISION SYSTEM FOR DETECTING ORCHARD TREES FROM UAV IMAGES
Bibliographic record
Abstract
Abstract. Orchard tree inventory plays an important role in acquiring up-to-date information on planted trees for effective treatments and crop insurance purposes. Determining tree damage could help assess orchards’ health faster and cheaper. Having accurate information on the tree’s status could also help managers to plan necessary fieldwork and predict productivity. Traditional orchard inventory is often performed manually, and thus is time-consuming, costly, and subject to error. An alternative is computer vision algorithms that could automatically detect orchard trees based on UAV imagery. The objective of this study is to develop a method using advanced computer vision algorithms to automatically detect apple trees on UAV multispectral images. This task is challenging since apple trees are overlapping over the UAV images, and hence distinguishing different crowns could be difficult. Motivated by the latest advances in UAV imagery and deep-learning models, addressed the tree detection problem by exploring the two CNN models YOLO (You Only Look Once) and DeepForest for detecting apple trees on UAV images. We first constructed a labelled dataset by dividing the study area into equally sized patches. Then we manually annotated all apple trees seen in RGB images. The annotated dataset was then randomly divided into three subsets (training, validation, and testing), for training and testing machine learning models. The performed experiments demonstrate the efficiency and validity of the proposed approach for orchard tree inventory. In particular, the proposed framework achieved a precision of 91% and an F1-score of 87% by adopting the DeepForest model for tree detection.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".