Estimation of Crop Biomass and Leaf Area Index from Multitemporal and Multispectral Imagery Using Machine Learning Approaches
Bibliographic record
Abstract
Accurate estimation of biomass and Leaf Area Index (LAI) requires appropriate models and predictor variables. These biophysical parameters are indicative of crop productivity, and thus, are of interest in applications such as crop yield forecasting and precision farming. This study evaluated the potential of leveraging vegetation indices derived from multi-temporal RapidEye data using a machine learning approach to estimate crop biomass and LAI. Both near-infrared and red-edge based indices were considered in this study. In-situ measurements of these two parameters for three main cash crops, including canola, corn, and soybeans, were collected during a field campaign and used for model calibration and validation. Crops models were developed using the artificial neural network (ANN) and support vectors regression (SVR). Results showed that, for each crop, the SVR modeled LAI and biomass more accurately than ANN. For biomass, the SVR’s Root Mean Square Errors (RMSEs) were reported as 25.22 g/m2 for canola, 88.13 g/m2 for corn, 5.91 g/m2 for soybean, and 56.14 g/m2 for all crops pooled. Similarly, for the LAI, SVR provided the best model with RMSE = 0.59 m2/m2 for canola, RMSE = 0.27 m2/m2 for corn, RMSE = 0.21 m2/m2 for soybean, and RMSE = 0.51 m2/m2 for all crops together.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".