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 (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R ^{2} =0.745$ </tex-math></inline-formula>, RMSE <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$= 4.499~\mu $ </tex-math></inline-formula>g/cm2, and RPD = 1.989) and CCC (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R ^{2} =0.822$ </tex-math></inline-formula>, RMSE <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$= 30.535~\mu $ </tex-math></inline-formula>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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R ^{2}$ </tex-math></inline-formula> 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".