PlotCam: A handheld proximal phenomics platform
Bibliographic record
Abstract
Future crop varieties must be higher yielding, stress resilient and climate agile to feed a larger population, and overcome the effects of climate change. This will only be achieved by a fusion of plant breeding with multiple “omic” sciences. Field-based, proximal phenomics assesses plant growth and responses to stress and agronomic treatments, in a given environment, over time and requires instruments capable of capturing data, quickly and reliably. We designed the PlotCam following the concepts of cost effective phenomics, being low-cost, light-weight (6.8 kg in total) and portable with rapid and repeatable data collection at high spatial resolution. The platform consisted of a telescoping, square carbon fiber unipod, which allowed for data collection from many heights. A folding arm held the sensor head at the nadir position over the plot, and an accelerometer in the arm ensured the sensor head was level at the time of data acquisition. A computer mounted on the unipod ran custom software for data collection. RGB images were taken with an 18 MP, WiFi controlled camera, infrared thermography data was captured with a 0.3 MP infrared camera, and canopy height measured with a 0.3 MP stereo depth camera. Incoming light and air temperature were logged with every image. New operators were quickly trained to gather reliable and repeatable data and an experienced operator could image up to 300 plots per hour. The PlotCam platform was not limited by field design or topography. Multiple identical PlotCams permitted the study of larger populations generating phenomic information useful in variety improvement. We present examples of data collected with the PlotCam over field soybean experiments to show the effectiveness of the platform.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.008 |
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".