A New Approach to Statistical Process Control: Identification of Outliers in Yield Maps
Why this work is in the frame
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Bibliographic record
Abstract
The tools of precision agriculture are of utmost importance in the Brazilian agribusiness, enabling increases in yields and reducing production costs. The use of harvest monitoring systems makes it possible due the possibility to identify pontual problems in an area, however, it becomes necessary to be working properly so it does not acquire incorrect information. Therefore, the purpose with this study was to propose a new approach to identify discrepant points in harvesting maps using statistical process control, as well as to define the best multiple of the standard deviation to identificate these points. The work was conducted during the soybean harvesting at São Geronimo farm in an area of 38 hectares in the municipality of Candido Mota, located in the the state of São Paulo. For gathering information, it was used a Stara crop monitoring system (model Topper Maps) set to record information during harvest in each three second. The productivity data were used to generate an individual control chart to identify points that were out of control so they could be removed. Two standard deviation multiples, that presented an average productivity closer to the average real productivity of the area, were selected. The multiples of the deviations that came closest were the 2σ and 3σ. Two multiples of standard deviation presented an average yield closer to the average real yield of the area. Individual control charts can be used to set control limits and identify possible discrepancies. The multiple of standard deviation 3σ presented information with greater reliability.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it