Application of smart sensors to monitor the interactive effects of temperature and lighting on plant growth in a simulated controlled environment facility
Bibliographic record
Abstract
This thesis explored the potential use of smart sensors to monitor and integrate multiple environmental factors that play crucial roles in the intricate dynamics of plant growth in response to varying environmental conditions, such as lighting and temperature in controlled environment crop production systems. \nA controlled environment chamber was designed and built to conduct experiments across a range of temperatures (15 - 35C) and under varying lighting duration (7, 10 and 14 h) and intensity (100 and 150 µmol/m2.s). A set of wireless smart sensors were used to monitor the environmental conditions, including air and soil temperatures, relative humidity, light intensity, and carbon dioxide level. The study demonstrated that the wireless smart sensors were effective in collecting reliable data for monitoring the environmental conditions, and sensor data could be fused to optimize the environmental conditions for plant growth. From the sensor data, it was found that both fresh biomass and dry biomass were significantly influenced by the three tested environmental factors. The highest biomass accumulation was observed at moderate temperatures (25-27C), with diminished growth at both lower and higher temperatures. Light duration and intensity were found to have a noticeable effect on biomass production, with longer lighting periods and higher intensity fostering greater fresh and dry biomass production. Similar effects of environmental conditions on leaf development were found: the best environmental condition was the moderate temperatures and long lighting duration and high light intensity. However, the benefits of increased lighting were modulated by temperature, indicating a complex interplay between these factors.\nThis study contributed valuable insights into the optimization of environmental parameters for plant cultivation in controlled environment systems through the use of smart sensors. The findings highlighted the importance of carefully balancing temperature and lighting conditions to maximize plant growth. The study demonstrated the successful use of an array of wireless smart sensors in monitoring multiple environmental parameters in controlled environment crop production and multiple sensor data could potentially be fused to optimize the environment in smart vertical farming (plant factories).
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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".