From Fields to Pixels: UAV Multispectral and Field-Captured RGB Imaging for High-Throughput Wheat Spike and Kernel Counting
Bibliographic record
Abstract
Wheat breeding enhances wheat crops for better environmental resistance and higher yield potential. Experimental breeding lines are evaluated based on their yield potential, where quantifying spikes per unit area and kernels per spike is crucial for assessment. This study introduces SPINEL (SPIke and kerNEL), a framework that combines unmanned aerial vehicle (UAV)-captured multispectral imaging and field-captured RGB camera imaging for spike and kernel quantification. This approach utilizes YOLOv8 models, each tailored for a specific detection task. The first model detects plots in UAV-captured multispectral images with a mean average precision (mAP) score of 95%, while the second model, trained to detect spikes in the same dataset, demonstrates an mAP score of 86%. The third model detects spikes and kernels in field-captured RGB images with an 85% mAP score. The first two models aid in estimating the spike density in each field plot. The third model provides the estimated number of kernels in spikes of each unique breeding line. Spikes per field plot and kernels per spike serve as key quantification metrics. The SPINEL framework utilizes the geolocation information of the multispectral images and associates these metrics with breeding lines at the field level. This integration provides a clear visual representation of spike count and average kernels per spike for each field plot. SPINEL offers a precise, automated solution for phenotyping in wheat breeding, promising significant advancements in crop improvement strategies.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".