Performance of cylindrical leaf wetness duration sensors in a tropical climate condition
Bibliographic record
Abstract
Leaf wetness duration (LWD) measurements are required for disease warning in several agricultural systems, since it is an important variable for the diagnose of plant disease epidemiology. The cylindrical sensor is an inexpensive and simple electronic LWD sensor initially designed to measure this variable for onions, however some studies show that it may be helpful for standard measurements in weather stations and also for different crops. Therefore, the objective of this study was to assess their performance under tropical climate conditions, in Brazil, having as standard measurements those obtained by flat plate sensors, which have presented very good performance when compared with visual observations. Before field assessments, all LWD sensors used in our study (flat plates and cylinders) were white latex painted and submitted to a heat treatment. Laboratory tests were performed in order to determine the resistance threshold for the sensor to be considered wet and the time response of the sensors to wetness. In the field, all cylindrical sensors were initially deployed horizontally 30-cm above a turfgrass surface in order to assess the variability among them with respect to LWD measurements. The variability among the horizontal cylindrical sensors was reduced by using a specific resistance threshold for each sensor. The mean coefficient of variation (CV) of LWD data measured by the cylindrical sensors was 9.7%. After that, the cylindrical sensors were deployed at five different angles: 0º, 15º, 30º, 45º, and 60º. Data of measurements made at these angles were compared with the standard measurement, obtained by flat plate sensors at the same height and installed at 45º. The deployment angle had no systematic effect on LWD measurements for the local tropical conditions, since the correlations between flat plate and elevated cylinder measurements were very high (R² > 0.91), which differed from the results obtained under temperate climatic conditions, where LWD measured by cylinders were two hours longer than by flat plate sensors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".