Optimizing Lettuce Crop Yield Prediction in an Indoor Aeroponic Vertical Farming System Using IoT-Integrated Machine Learning Regression Models
Bibliographic record
Abstract
The rise in agricultural innovation has led to the use of sustainable farming practices, such as aeroponics, which increase crop production.Aeroponics, a soil-free indoor precision farming system, cultivates crops using vertical towers, garnering global attention for its environmentally friendly and productive cultivation methods.Aeroponic systems can grow lettuce, a popular green-leafy vegetable, quickly and with minimal water usage.However, yield prediction is a tedious task in real-world scenarios.To efficiently predict lettuce yield, various scientific experiments have integrated IoT and machine-learning techniques.This research work utilized various machine-learning regression models, including linear, support vector, random forest, and XGBoost, to estimate lettuce yield based on specific growth parameters such as pH, EC, temperature, total dissolved salts (TDS), turbidity, humidity and light.After implementation, the results showed a high prediction accuracy of 93% and minimal error rates produced by the XGBoost regression model when compared with the other regression models.Further, fine-tuning the model parameters enhanced the XGBoost model's performance, enhancing its generalization capability to handle new realtime data.This indicates that optimizing the lettuce yield involves not only using indoor aeroponic farming methods but also utilizing advanced sustainable food production systems.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".