Crop Yield Estimation of Teff (Eragrostis tef Zuccagni) Using Geospatial Technology and Machine Learning Algorithm in the Central Highlands of Ethiopia
Bibliographic record
Abstract
The genus Eragrostis tef Zuccagni is commonly known as “Teff”, is an indigenous cereal crop and is the major staple food crop in Ethiopia. It is mostly used to prepare a spongy flatbread called “Injera” and is consumed by more than 70% of the Ethiopian people. This study is conducted at nine Teff-dominated zones of the country to examine whether geospatial technology can serve to estimate the productivity of crop yield. For this, ground truth sample plots were used for nine zones, and geospatial technology and machine learning were applied for upscaling to the whole study area’s scale. Very good correlation results were obtained from spatial predictions of Teff yield for 2015 and 2020 with ROC-AUC of 89 and 91% and R2 of 0.67 and 0.73, respectively. The average predicted yields of Teff were about 1.37 t/ha and 1.99 t/ha for 2015 and 20202, respectively, indicating that such technology can offer a very good result to estimate yields for unreachable areas in the case of either during unfavorable political or other natural conditions. By doing so, we can plan to apply such technologies that can serve to save time, effort, and resources.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".