Deep Learning Approach for Forecasting Apple Yield using Soil Parameters
Bibliographic record
Abstract
Procuring apple yield prior to harvest is essential since it helps in estimating the apple production and prices. A compound Deep Learning (DL) model, SeriesNet with Gated Recurrent Unit (GRU) and Attention (Att-SeriesNet-GRU), is used in this work to predict the apple yield for 15 counties across 6 different Crop Reporting Districts (CRD) in California. The DL model is trained using static soil parameters, which remain constant over years per county and dynamic parameters, which change daily or monthly for a specific county as input, and the corresponding annual apple yield for that county as output. If the training is done based on a single county data then the static parameters won’t add information to the DL model since they remain constant over years per county. Therefore, considering different counties across California is decided to study the effect of considering the static soil parameters along with the dynamic ones. The county level annual apple yield forecast using both static and dynamic parameters together gives promising results. Experimenting with the test set as input shows that adding the static parameters together with the dynamic ones gives an improvement of around 34% in the value of Aggregated Measure (AGM) over the case of using the dynamic parameters alone for yield forecasting. It is also found that training the DL model with augmented training set improves the AGM value by around 12%.
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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".