Leveraging Artificial Intelligence for Large-Scale Plant Phenology Studies From Noisy Time-Lapse Images
Bibliographic record
Abstract
Phenology has become a field of growing importance due to the increasingly apparent impacts of climate change. However, the time-consuming, subjective and tedious nature of traditional human field observations have hindered the development of large-scale phenology networks. Such networks are rare and rely on time-lapse cameras and simplistic color indexes to monitor phenology. To automatize rapid, detailed and repeatable analyzes, we propose an Artificial Intelligence (AI) framework based on machine learning and computer vision techniques. Our approach extracts multiple ecologically-relevant indicators from time-lapse digital photography datasets. The proposed framework consists of three main components: (i) a random forest model to automatically select relevant images based on color information; (ii) a convolutional neural network (CNN) to identify and localize open tree buds; and (iii) a density-based spatial clustering algorithm to cluster open bud detections across the time-series. We tested this framework on a dataset including thousands of black spruce and balsam fir tree images captured using our phenological camera network. The performed experiments showed the efficiency of the proposed approach under challenging perturbation factors, such as significant image noise. Our framework is exceedingly faster and more accurate than human analysts, reducing the time-series processing time from multiple days to under an hour. The proposed methodology is particularly appropriate for large-scale and long-term analyzes of ecological imagery datasets. Our work demonstrates that the use of computer vision and machine learning methods represents a promising direction for the implementation of national, continental, or even global plant phenology networks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".