Assessment of Ripening Degree of Avocado by Electrical Impedance Spectroscopy and Support Vector Machine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Avocado, a climacteric fruit, exerts high rate of respiration and ethylene production and thereby subject to ripening during storage. Therefore, its ripening is a significant factor to impart optimum quality in postharvest storage. To understand the dynamics of ripening and to assess the degree of ripening in the avocado, electrical sensing technique is utilized in this study. In particular, electrical impedance spectroscopy (EIS) is found to uncover the physiological and structural characteristics in plants and vegetables and to follow physiological progressions due to environmental impacts. In this work, we present an approach that will integrate EIS and machine learning technique that allows us to monitor the ripening degree of the avocado. It is evident from our study that the impedance absolute magnitude of the avocado gradually decreases as the ripening stages (firm, breaking, ripe, and overripe) proceed at a particular frequency. In addition, principal component analysis shows that impedance magnitude (two principal components combined explain 99.95% variation) has better discrimination capabilities for ripening degrees compared to impedance phase angle, impedance real part, and impedance imaginary part. Our classifier utilizes two principal component features over 100 EIS responses and demonstrates classification over firm, breaking, ripe, and overripe stages with an accuracy of 90%, precision of 93%, recall of 90%, f1-score of 90%, and auc of 88%. The study offers plant scientists a low cost and nondestructive approach to monitor postharvest ripening process for quality control during storage.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 it