Bilinear LSTM with Bayesian Gaussian Optimization for Predicting Tomato Plant Disease Using Meteorological Parameters
Bibliographic record
Abstract
Climate change threatens agriculture; as a result, adaptation measures are required to withstand agricultural produce, reduce susceptibility, and improve the farm system's flexibility to climate change.Meteorological parameters like temperature and relative humidity play an essential role in the condition of disease occurrence in plants.We studied ARIMA, Prophet, and Long Short-Term Memory (LSTM) with stochastic gradient descent with momentum, RMSprop, and Adam optimizers to forecast the temperature and relative humidity.The work proposes a hybrid regression prediction model of Bilinear LSTM with Gaussian Bayesian optimization (BLSTM_bayOpt) for predicting disease in tomato plants based on weather parameters.From the six prediction models in this study, the performance of BLSTM_bayOpt in prediction with RMSE of 1.1573 and 5.5509, MAPE is 0.0556 and 0.0927, R2 is 0.9324 and 0.9475 for temperature and relative humidity, respectively.The proposed hybrid BLSTM_bayOpt model improved by 40.67%, with an MSE score for relative humidity prediction.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".