Estimation of Water Hyacinth Using Computer Vision
Bibliographic record
Abstract
The different controls of water hyacinth, an invasive species of tropical and subtropical environ-ments, have demonstrated some limitations requiring additional monitoring tasks to maintain the ecological balance. Therefore, quantifying and valuing this aquatic biomass becomes a sustainable management alternative. However, the water hyacinth estimation remains a challenging task in developing countries with regard to the used methods: empirical relationships between yield and production indices calculated experimentally, structural parameters measured or calculated through specific experiments (not dynamic), etc. These methods lose precision depending on the type of plant, cultural methods and practices and the seasons. Then, it becomes urgent to develop a dynamic estimation method with a proven track record of reliability despite the inconsistency of the factors mentioned above. This article contributes to the improvement of aquatic biomass estimation by proposing a Computer Vision based solution for estimating fresh mass of water hyacinth. To achieve this goal, the morphology of the species is assessed and an XML classifier is developed. This model is then implemented in a mobile app facilitating its end use. The proposed algorithm demonstrated a mean average precision of 96.89%. Considering the recorded level of accurateness, the developed method can be used to estimate different types of biomass.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".