Non-destructive estimation of potato leaf chlorophyll and protein contents from hyperspectral measurements using the PROSPECT radiative transfer model
Bibliographic record
Abstract
Optimizing nitrogen (N) fertilization in potato (Solanum tuberosum L.) production by in-season measurements of potato N status may improve fertilizer N-use efficiency. Hyperspectral leaf reflectance and transmittance measurements can be used to assess potato N status by estimating leaf chlorophyll or N contents. This study evaluated the ability of the inverted PROSPECT radiative transfer model to predict leaf chlorophyll and N (as protein) contents. Trials were conducted with Russet Burbank and Shepody potato cultivars under different N fertility rates (0 to 300 kg N ha-1) in 2001 and 2002. Leaf reflectance and transmittance, leaf chlorophyll content, and leaf protein content were measured. Leaf chlorophyll and protein content correlated significantly (r = 0.16*, n = 584), but the relationship was strongly dependent on sampling date (r = 0.55* to 0.92*). Chlorophyll content was predicted with reasonable accuracy by the model, particularly in 2002. The low estimation accuracy in 2001 was probably related to sample variability induced by prolonged drought conditions. Protein content could not be predicted with any degree of accuracy by the model. The relative success of the PROSPECT model to predict chlorophyll content, and the good correlation between leaf chlorophyll and leaf N, suggests that it might be used as a component of a more complex leaf-canopy reflectance model to estimate chlorophyll content from reflectance spectra at the canopy level. Key words: Leaf reflectance, PROSPECT radiative transfer model, Solanum tuberosum
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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 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".