UAV-Based Computer Vision System for Orchard Apple Tree Detection and Health Assessment
Bibliographic record
Abstract
Accurate and efficient orchard tree inventories are essential for acquiring up-to-date information, which is necessary for effective treatments and crop insurance purposes. Surveying orchard trees, including tasks such as counting, locating, and assessing health status, plays a vital role in predicting production volumes and facilitating orchard management. However, traditional manual inventories are known to be labor-intensive, expensive, and prone to errors. Motivated by recent advancements in UAV imagery and computer vision methods, we propose a UAV-based computer vision framework for individual tree detection and health assessment. Our proposed approach follows a two-stage process. Firstly, we propose a tree detection model by employing a hard negative mining strategy using RGB UAV images. Subsequently, we address the health classification problem by leveraging multi-band imagery-derived vegetation indices. The proposed framework achieves an F1-score of 86.24% for tree detection and an overall accuracy of 97.52% for tree health assessment. Our study demonstrates the robustness of the proposed framework in accurately assessing orchard tree health from UAV images. Moreover, the proposed approach holds potential for application in various other plantation settings, enabling plant detection and health assessment using UAV imagery.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".