Enhancing Fresh Produce Yield Forecasting Using Vegetation Indices from Satellite Images
Bibliographic record
Abstract
Developing fresh produce yield forecasting service is essential for estimating fair prices to protect against overpriced agricultural commodities and minimize the bid ask spread which not only benefits the retailers and customers but also protects farmers. Forecasting the fresh produce yield is achieved using state of the art deep learning (DL) models. Those models are trained and built using data retrieved from Santa Barbara region in California using an ensemble of Attention Deep Feedforward Neural Network with Gated Recurrent Units (GRU) and Deep Feedforward Neural Network with embedded GRU units. The ensemble takes as input the soil moisture and temperature parameters as well as vegetation indices (VIs) calculated from images retrieved from multiple satellites. The effect of adding the VIs as input parameters on the forecasting performance of the deep learning model is assessed and the most effective VIs are selected. In addition, interpolation techniques are used to estimate the missing VIs due to the low frequency of capturing the images by the satellites. A comparative analysis is conducted to choose the most effective technique, which is found to be Cubic Spline interpolation. One VI, which is the Normalized Difference Vegetation Index (NDVI), proves to be the most effective index in forecasting the yield. Based on the aggregated error measure (AGM) score, the yield forecasting performance of the DL ensemble is enhanced by 12.51% after adding the complete interpolated NDVI to the input parameters used in training the 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) | 0.001 | 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".