Hyperspectral Remote Sensing of Potato Plant Nutrient Deprivation and Vegetation Stress using High-Resolution Spectroradiometry for Minimal Input Agricultural Systems
Bibliographic record
Abstract
Abstract. Nitrogen is a plant growth limiting nutrient in natural ecosystems, however, many agricultural systems are saturated due to high fertilizer applications. This can lead to higher costs, nitrogen leakage into the environment and unnecessary GHG emissions that contribute to climate change. To avoid this, Neilson (2021) has proposed an approach based on minimal input agricultural systems (MIAS). MIAS seeks to grow crops with limited fertilizer inputs. This is accomplished through targeted/precision fertilizer placement, managing plant physiology to operate at higher efficiencies and adopting new varieties. To work optimally MIAS requires methods to quickly assess plant nutrient status and adjust fertilizer applications accordingly. This study tested remote sensing for detecting stress in potato plants, based on two separate and independent laboratory remote sensing experiments. The goal is to determine minimal input levels applied to a starvation agricultural system that provide yields equivalent (or perhaps even improving upon) those obtained using current excessive inputs, both in terms of yield and, importantly, quality. This research is at the front-end of a proposed paradigm shifting new approach to agriculture. We are being careful to start at first principles in this work; thus the study presented here is based on multiple trials and independent tests. Results testing experimental approaches and N deprivation assessment determined the optimal leaf density and timing of measurements and demonstrated a capability to detect vegetation stress and N deprivation in three potato varieties. These results will inform next steps for future RPAS/UAV/airborne/satellite studies and be used to develop other plant physiology assessment methods.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".