Application of hyperspectral remote sensing in stress detection and crop growth modeling in corn fields
Bibliographic record
Abstract
This study used hyperspectral data to determine nitrogen, weed, and water stresses in a corn (Zea mays L.) field in southwestern Quebec, and incorporated these data in crop growth models for better crop growth simulation under stressful conditions. In 2000, aerial hyperspectral images (72 wavebands, ranging from 407 to 949 nm) were acquired, and analyzed using a stepwise approach to identify wavebands useful in detecting weed and nitrogen stresses. Discriminant analysis (DA) was used to classify different weed and nitrogen treatments and their combinations. This analysis showed greater classification accuracy (nearly 75%) than those obtained with artificial neural networks (58%) or decision tree algorithms (60%), at the initial growth stages, the time when remedial actions are most needed to alleviate weed and nitrogen stresses. To explore the possibility of improving nitrogen stress detection in corn in the presence of a confounding water stress, ground-based 2151 narrow-waveband reflectance values (350 to 2500 nm), were collected in 2002. Using DA with the chosen subset of narrow-wavebands, a classification accuracy of greater than 95% was obtained. For crop growth monitoring, the STICS model was evaluated for yield and biomass estimation in cornfields under different stressful growth conditions using the data collected from 2000 to 2002. Measured yield, biomass, and leaf area index (LAI) were used for both calibration and validation of the model. High correlation coefficients between the measured and estimated grain yield (0.96), biomass (0.98), and LAI (0.93) indicated that the model has good potential in the simulation of corn growth. The model was also linked with LAI values estimated from the hyperspectral observations using the Support Vector Machines technique. Coupling STICS with remote sensing resulted in an overall improvement in the simulation of corn yield (6.3%) and biomass (3.7%). A new approach was developed to apply crop growth models for yield estimation in weedy areas. The proposed method first corrects the measured/estimated LAI values in weed infested fields for weed effect, and then uses the corrected LAI values as input to the crop growth model. The results showed that the crop yield and biomass predictions were correctly simulated by this method.* *This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation).
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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 | 0.000 |
| 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 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".