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, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> 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, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> homes and grocery stores, that have diverse illuminations.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".