Wireless Sensor Network and Deep Learning For Prediction Greenhouse Environments
Bibliographic record
Abstract
Greenhouses are anti-seasonal. Particularly in regions with adverse climate conditions. Controlling, monitoring and predicting a greenhouse is important to allow optimal growth condition for crops. However, testing the greenhouse for real atmosphere requires a lot of time, effort and money. The modeling and simulation approach is best suited to predict thereby improve the greenhouse environment. This paper presents a model for predicting environmental atmosphere for producing tomatoes in greenhouse. Several factor such as: air temperature, humidity, barometric pressure, and dew point are needed to be monitered. Since atmosphere pattern are complex and are a nonlinear system, the customary methods for prediction are inefficient and ineffective. Recurrent neural network (RNN) with long short-term memory are a solution. The proposed RNN evaluate the performance of the model by using different neurons, hidden layers and transfer functions to predict the environmental parameters of the greenhouse for an entire year ahead. By utilizing the Root Mean square error (RMSE) to evaluate the performance of the proposed model, results show our model has very low RMSE and time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".