Enabling Consumer UAVs for Precision Agriculture Applications: A Case Study of Yield Estimation
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) equipped with various sensors and onboard processing capabilities have emerged as a promising means to acquire field data for precision agriculture applications. However, such UAVs are costly, restricting their deployment in small-to-medium-sized fields, particularly in developing countries. In contrast, consumer-grade UAVs have high-resolution RGB cameras and video streaming abilities at affordable prices. This paper presents an efficient processing pipeline to analyze video streams from consumer-grade UAVs on smartphones. The processing pipeline consists of preprocessing, object detection, and yield estimation. The object detector, being the most computationally expensive module, is invoked every nth frame due to video redundancy and the target platform’s limited resources. The yield estimation task on a smartphone requires efficient and accurate fruit detection, which a modified YOLOv8n model achieved. We evaluate our pipeline on datasets of apple and peach trees and demonstrate that it can process UAV-captured images to collect yield-related statistics. We also discuss the lessons learned and outline future directions for consumer-grade UAV-based precision agriculture applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".