A Deep Learning Approach to Detect the Spoiled Fruits
Bibliographic record
Abstract
Fruits are one of the vital sources of nutrients for the mankind and their life span is very less. The fruit spoilage may occur at various stages such as, at the harvest time, during transportation, during storage etc. Freshness is a parameter used for accessing the quality of the fruit. About 20% of the harvested fruits are spoiled due to many factors, before consumption by humans. The spoilage of one fruit has a direct impact on the neighboring fruits. It is also a one of the indicators that gives an estimation of number of days that a fruit can be preserved. Early identification of the spoilage helps in taking the appropriate measures for the removal of spoiled fruits from the whole lot. So that it helps in preventing the spread of spoilage to its adjacent fruits. Deep learning based technological advancements helps in automatically identifying the spoiled fruits. In this work, internal quality attributes of the fruit are not taken into consideration for spoilage detection, only the external attributes are considered. The supervised learning technique is employed for the freshness analysis of two different types of fruits, Apple and Banana. As the 2 varieties are involved, it is a multiclass classification model with 4 classes. One shot detection technique is employed to accurately classify among the good fruit and spoiled fruit. Few images in the dataset are obtained from the kaggle.com and the rest are self - captured images. The dataset is balanced to avoid the biasness in the model. The model is implemented using Yolov4 and tiny Yolov4 frame works. These are one shot detection techniques, can be used for real time deployment. The inferences were obtained on the real time images and video. Confusion matrix is tabulated the performance metrics such as accuracy, F1 Score and recall are discussed with respect to these two techniques.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".