Smart Harvest: Web-Integrated Ripeness Detection for Apples with CNN Algorithm
Bibliographic record
Abstract
This study presents "Smart Harvest," a web-based system that aids farmers, sellers, or buyers in identifying the maturity level of apples based on skin colour using a Convolutional Neural Network (CNN) algorithm.Traditional methods, reliant on human labour, suffer from subjectivity and inconsistency in evaluating fruit maturity.Our system offers an automated, objective, and reliable alternative to address this.By analyzing skin colour, Smart Harvest classifies apples into two primary maturity levels: raw and ripe.Through rigorous training and testing phases, the system has demonstrated remarkable efficiency, achieving an impressive average prediction accuracy of 93-94%.This paper details the development and deployment of Smart Harvest, showcasing its potential to enhance agricultural productivity by providing a user-friendly, web-based tool for accurate ripeness detection.The implementation of such a system not only promises to standardize the maturity assessment process but also aims to optimize harvesting schedules, reduce labour costs, and minimize waste, thereby contributing significantly to the agricultural sector's sustainability and profitability.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".