Orchard Apple Tree Health Assessment using UAV Imagery-Based \nComputer Vision System
Bibliographic record
Abstract
Accurate and efficient orchard tree inventories play a crucial role in obtaining up-to-date information for effective treatments and crop insurance purposes. Surveying orchard trees, including counting, locating, and assessing their health status, is vital for predicting production volumes and \nfacilitating orchard management. However, traditional manual inventories are labor-intensive, expensive, and prone to errors. Motivated by the recent advances in UAV imagery and computer \nvision methods, we propose a new framework for individual tree detection and health assessment. \nThe proposed approach follows a two-stage process. First, we build a tree detection model based \non a hard negative mining strategy using RGB UAV images. In the second stage, we address the \nhealth classification problem using two methods. We present a classical machine learning approach \nby exploring the use of multi-band imagery-derived vegetation indices. We also propose a new convolutional autoencoder-based architecture mainly designed to extract the relevant features for tree \nhealth classification. \nThe performed experiments demonstrate the robustness of the proposed framework for orchard \ntree health assessment from UAV images. In particular, our framework achieves an F1-score of 86.24% for tree detection and an overall accuracy of 98.06% for tree health assessment. Moreover, our work could be generalized for a wide range of UAV applications involving a detection/classification process.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".