A Sustainable Learning Framework: UAV-Based Oat Chlorophyll Monitoring Using Radiative Transfer Models and Deep Learning Techniques
Bibliographic record
Abstract
Accurately and efficiently estimating leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC) is crucial for assessing photosynthetic productivity and monitoring crop growth in agroecosystems. The unmanned aerial vehicle (UAV)-based multispectral sensors, combined with machine learning models, offer high-precision solutions for estimating crop chlorophyll. However, several challenges, such as model performance limited by ground-truth data and issues with spatiotemporal scalability and transferability, hinder the broader application of these methods. This study proposed a transferable pretraining framework (PROSAIL-StDNN), which leveraged knowledge from PROSAIL simulations through three deep learning models to improve the accuracy of monitoring oat LCC and CCC using UAV-acquired multispectral data. The results indicated that PROSAIL-StDNN achieved the highest accuracy in predicting LCC ($R ^{2} =0.745$, RMSE$= 4.499~\mu $g/cm2, and RPD = 1.989) and CCC ($R ^{2} =0.822$, RMSE$= 30.535~\mu $g/cm2, and RPD = 2.378). Relative to common statistical models, PROSAIL-StDNN reduced LCC RMSE by 10.11%–15.86% and CCC RMSE by 11.68%–22.30%. Additionally, the PROSAIL-StDNN model demonstrated significant spatial and temporal transferability after secondary fine-tuning with data from two additional field sites, achieving$R ^{2}$values around or above 0.8 for both LCC and CCC. This framework offers a transferable approach for advancing UAV-based monitoring of crop physiological traits in agricultural practice.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".