Determining the Beginning of Potato Tuberization Period Using Plant Height Detected by Drone for Irrigation Purposes
Bibliographic record
Abstract
Insolation and precipitation instability associated with climate change affects plant development patterns and water demand. The potato root system and soil properties lead to water vulnerability, impacting crop yield. Regarding potato physiology, plants stop growing when the root depth stabilizes, and then the tuberization period begins. Since this moment, water supply is required. Consequently, an approach based on plant physiology may enable farmers to detect the beginning of the irrigation period precisely. Remote sensing is a fast and precise method for obtaining surface information using non-invasive data collection. The database comprises root depth (RD) and plant height (H) data collected during 2019, 2020, and 2021. This research aims to develop a dynamic approach based on remote sensing and crop physiology to accurately determine the beginning of the tuberization period, called here the irrigation critical point (ICP). The results indicate a high correlation between RD and H (>0.85) which is independent of in-field soil and relief variations > 0.95). Further, plant growth rate corroborates the correlation results with decreasing patterns in time (R2 > 0.80), independent of environmental variations. In short, it was possible to determine the ICP based on the crop growth dynamics, independently of climate variations, field placement, or irrigation system.
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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".