Bundling Agricultural Services under Seeing Is Believing and Plantwise: Benefits and Opportunities
Bibliographic record
Abstract
The Seeing is Believing (SIB) project builds on the Plantwise (PW) programme and provides picture-based advisories (PBA), i.e. remote advice to farmers based on picture-based crop monitoring.Farmers registered in the SIB project send images of affected crops and repeat images of the fields via mobile phone using an app.Plant doctors assess the images and provide plant health advice to farmers by messages on their registered mobile numbers.Farmers are provided with different management options, based on the severity of crop damage.During the third season of the project, 350 farmers from 70 villages in Pudukottai and Thanjavur, were targeted with PBA.175 farmers received a bundled service of PBA and picture-based insurance (PBI), with insurance pay-outs based on any visible damage in the field images uploaded by the farmers.The other 175 farmers only received PBA.Plant clinics (PC) were run in all the 70 village locations covered under SIB.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.004 |
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".