Early Detection of Soybean Plant Injury from Glyphosate by Measuring Chlorophyll Reflectance and Fluorescence
Bibliographic record
Abstract
Early detection of crop injury from off-target drift of herbicide is critical in crop production. Subtle changes in canopy reflectance could present useful information to detect the onset of crop stress. This study was conducted in a greenhouse to evaluate a portable spectroradiometer and a portable chlorophyll fluorometer for the detection of crop injury caused by glyphosate spray. Non-glyphosate resistant soybean (Glycine max L. Merr.) plants were sprayed with glyphosate using a pneumatic track sprayer in a spray chamber. Four plants received a rate of 0.86 kg ae/ha glyphosate and four plants received 0.086 kg ae/ha. Additional four non-sprayed plants were used as controls. After the glyphosate spray, the chlorophyll reflectance of the plants was measured with the spectroradiometer at 4, 24, 48, and 72 hours to determine the plant response to herbicide. Simultaneously, fluorescence induction kinetics of the crop under stress was measured with the portable chlorophyll fluorometer. Results of the statistical mean separation indicated that the plant chlorophyll reflectance measurement could be used to differentiate crop stress from glyphosate at 24 hours after spray among treatments and to identify the effect of herbicide at 24 hours after spray in each treatment. Moreover, linear discriminant analysis with the reflectance data showed that the crop stress of the soybean plants from glyphosate could be identified at 24 hours or more post application. Results of the statistical mean separation also indicated that use of plant chlorophyll fluorescence measurement could not differentiate crop stress until 48 hours after spray among treatments while it could identify the effect of herbicide 24 hours after spray in each treatment. These findings demonstrate that chlorophyll reflectance and fluorescence measurements both could be used for early detection of crop stress.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".