Deep Learning Models for Strawberry Yield and Price Forecasting Using Satellite Images
Bibliographic record
Abstract
Forecasting crop yields and prices is crucial for both global food security and providing farmers with valuable information to avoid a price crash. This work proposes a hybrid deep learning model that uses satellite images to forecast strawberry yield along with farmers’ prices, applied in three counties in California. For tractability, a dimensionality reduction technique is applied by converting the images to histograms representing the pixel frequency. The models tested are Convolutional Neural Network (CNN), Variational AutoEncoder (VAE), CNN-Long Short-Term Memory (CNN-LSTM), Stacked AutoEncoder (SAE), and a voting ensemble of CNN-LSTM and SAE. It is found that the proposed voting ensemble of CNN-LSTM and SAE is the best at forecasting the daily strawberry yields and prices in all three counties. Based on an aggregated performance measure (AGM), the voting ensemble model outperforms the models suggested in literature with up to 70% forecasting improvement compared to the CNN model and up to 22% improvement over the CNN-LSTM model.
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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".