Classification of Harvesting Age of Mango Based on NIR Spectra Using Machine Learning Algorithms
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Bibliographic record
Abstract
The established assessment of post-harvest attributes, such as the age of harvesting day, requires destructive sampling that the availability of fruit of trees can often limit and is expensive.In contrast, non-destructive post-harvest attribute assessment utilizing the NIR data spectrum is fast and reliable, especially for mango.However, NIR spectral data frequently produce non-linearity with the reference dataset used.Therefore, this study conducted research on using NIR spectral data to classify the harvesting age of mango fruits using machine learning algorithms.A total of five supervised machine learning algorithms were explored to generate the classification model, including gradient boost (GB), k-nearest neighbor (k-NN), decision tree (DT), random forest (RF), and linear discriminant analysis (LDA).In this study, 237 NIR spectral data from mango fruits with Arumanis cultivars from orchard sites in the Garut district, West Java Province (Indonesia) were measured to determine the appropriate harvest time using NIR spectra 1000 to 2500 nm.The data sets were randomly divided into training and testing datasets, 80% and 20%, respectively.Hyperparameter optimization was performed using the GridSearchCV function from scikit-learn by observing the evaluation of the confusion matrix.Generally, all machine learning algorithms can show performance in classifying the harvest age of mango fruit based on NIR spectra data.Based on the accuracy evaluation matrix, the best machine learning algorithm arranged to classify the age of mango fruit harvest is DT>GB>LDA>RF>k-NN.Finally, predictions generated using the DT algorithm from more established machine learning algorithms as a training and testing set consistently yielded higher prediction accuracy in classification models.This study provides a framework for understanding the feasibility of machine learning algorithms on NIR data spectral to the accuracy of classification prediction of the harvesting age of mango.In addition, this study presents the importance of assessing the performance of the classification model using confusion metrics.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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 it