Comparison of different machine learning algorithms for predicting maize grain yield using UAV-based hyperspectral images
Bibliographic record
Abstract
Timely and accurately predicting maize grain yields will contribute to making adaptive measures to improve management practice and to adjust consumption patterns for ensuring food security. Unmanned aerial vehicles (UAV) are widely used to obtain high-temporal and high-spatial resolution remote sensing images of crops, enabling a possible sensor performance comparison. To date, few studies have compared the potential abilities of multispectral-based and hyperspectral-based images, only sensitive spectral wavelength and full hyperspectral spectra, and various machine learning approaches in estimating physiological characteristics such as chlorophyll meter values, leaf area index (LAI), and agricultural grain yields in high vegetation coverage. In this study, the multispectral and hyperspectral images with the ground measurement of crop traits were collected on 13 and 22 September 2021 in Nanpi experimental station, CangZhou, China. The potential ability of multispectral and hyperspectral images for estimating chlorophyll meter values, retrieving LAI, and predicting maize grain yields were explored and compared using the formed two-band (2D) vegetation indices (VIs) and 2D textural indices (TIs). The sensitive spectral wavelengths were confirmed using correlation analyses, then the sensitive spectral wavelength formed VIs and the full hyperspectral spectra were also compared for predicting maize grain yield using five commonly applied machine learning approaches and five deep learning approaches of convolutional neural network (CNN). The results indicated the narrow bands of hyperspectral remained high sensitive with chlorophyll meter values, leaf area index (LAI), and agricultural grain yields than multispectral images in high vegetation coverage. The adoption of full hyperspectral spectra significantly improved the accuracy of maize grain yield predictions compared with adopting VIs built only using sensitivity spectral wavelength. Based on selected VIs, random forest regression (RF) and LightGBM achieved the highest accuracy, R2 (RMSE) were 0.90 (0.55 t/ha), and 0.85 (0.59 t/ha), respectively. While based on full hyperspectral spectra, RF and CNN150 performed the best, with R2 (RMSE) being 0.92 (0.53 t/ha), and 0.91 (0.59 t/ha), respectively. This research concluded the integration of full hyperspectral spectra in combination with RF were highly recommended for predicting maize grain yields, especially for crops in high vegetation coverage.
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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 | 0.000 | 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".