RipeTrack: Assessing Fruit Ripeness and Remaining Lifetime Using Smartphones
Bibliographic record
Abstract
Several studies have shown that a significant fraction of fresh fruits is discarded at the retail and consumer levels, wasting precious resources, polluting the environment, and contributing to increased food prices. An important factor contributing to this problem is the lack of scalable solutions for determining fruit ripeness and remaining lifetime. We propose a cost-effective solution that leverages the sensing capabilities of phones and machine learning models to analyze the optical properties of fruits at various ripening stages. The proposed solution is non-invasive, works for different fruits, and produces intuitive outputs,e.g.Unripe/Ripe/Expired and the percentage of remaining lifetime, enabling retailers and consumers to minimize food waste. We implement a proof-of-concept mobile application, RipeTrack, and demonstrate the accuracy and robustness of the proposed approach using an extensive empirical study with multiple fruits, including avocados, pears, bananas, nectarines, and mangoes. Our results show, for example, that RipeTrack can identify the ripeness level of avocados and pears with an accuracy of 95% and 98%, respectively, and it can predict their remaining lifetimes with an accuracy of 93% and 97%. Our results also show that RipeTrack can easily be extended to new fruits using transfer learning, and it functions in realistic environments,e.g.homes and grocery stores, that have diverse illuminations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".