Hyperspectral imaging: A potential tool for monitoring crop infestation, crop yield and macronutrient analysis, with special emphasis to Oilseed Brassica
Bibliographic record
Abstract
Due to increase in human population at an alarming rate, there is tremendous pressure on the agriculture sector for increasing production of agricultural commodities. Oilseed Brassica is an important oilseed crops in India. Although, India occupies third position with about 10.3 % share in the acreage, and production of rapeseed-mustard in the world after China, and Canada, with the average yield of 1188 kg per hectare, which is low as compared to world average 1994 kg/ha. The major reasons for low exploitable yield are infestation by various pathogens, and pests, improper weed management, degradation of soils due to excessive use of pesticides, fertilizers, and emerging pesticide resistance. Hyperspectral imaging system (HIS), also known as imaging spectroscopy or 3D spectroscopy, combines imaging, and spectroscopy into a single system. Through its multi-spectral, multi-temporal, and multi-resolution observation capability, the technology provides an alternative to traditional methods for facilitating sustainable agriculture by mapping, and monitoring the agricultural situation, retrieval of biophysical parameter, and management/ decision support for agricultural development. For oilseed rape, the technology has been found to be useful for disease forecasting, monitoring infestation induced damages, predicting seed yield, detection of fungal pathogens, weeds and macronutrient analysis for monitoring fertilizer application.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".