Comparative Analysis of Classical Machine Learning and Deep Learning Methods for Fruit Image Recognition and Classification
Bibliographic record
Abstract
In this investigation, the crucial role of fruits in daily lives is acknowledged, with emphasis placed on their significance in nutrition and agriculture.The primary focus is directed towards fruit image recognition and classification, a task of paramount importance in the present context.To expound on the methodology, classical machine learning approaches, encompassing K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Decision Trees (DT), are leveraged.Additionally, the capabilities of deep learning are harnessed through the utilization of the AlexNet model.The dataset selected, Fruit-360, is widely acknowledged and utilized, underscoring its popularity and relevance within the research community.Of particular note in the findings is the exceptional performance of the AlexNet model, with the highest metrics in accuracy (99.85%), precision (99.92%), sensitivity (99.86%), and an impressive F1 score (99.89%) when compared to all tested algorithms.The effectiveness of deep learning, especially in tasks revolving around imagebased classification, is underscored by these results.The impact of these noteworthy results transcends multiple domains.In agriculture, the potential for automated fruit sorting holds the promise of heightened efficiency and decreased waste.Similarly, in healthcare, the integration of fruit recognition into dietary and nutritional assessments presents a substantial opportunity.A thorough outlook on the progression of fruit recognition and classification is encapsulated by this study, offering a positive outlook for the future in these fields.
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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 | 0.000 |
| 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 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".