Apple Fruit Classification and Damage Detection Using Pre-trained Deep Neural Network as Feature Extractor

Kapila, Gurucharan; Vandana, B.; Khaitan, Ayush; Avinash, A. Francis; Kumar, C. H. Ajay

doi:10.1007/978-981-16-8512-5_26

Cited by 3 publications

(1 citation statement)

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“…The paper by Kapila et al [31] proposes a support vector machine (SVM) classifier utilizing hierarchical features taken from the entirely associated layer of the pre-trained deep convolutional neural network as a classification model for various apple fruit kinds. The accuracy, precision, recall, and F1-score of the suggested classification model are evaluated in performance metrics.…”

Section: Detailed Accuracy By Class In Terms Of Precision Recall and ...mentioning

confidence: 99%

Naive Bayes Classifier in Grading Carabao Mangoes

Guillergan,

Sabay,

Madrigal

et al. 2024

Technium

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This study explores machine learning’s potential to classify carabao mangoes, a key Philippine export, into four grades based on size: A (large), B (medium), C (small), and R (reject). It introduces a Naïve Bayes classification model that uses image processing to extract features for grading. The goal is to create a consistent grading system to enhance export efficiency and benefit local farmers. The research aims to validate the Naïve Bayes model’s accuracy using size, weight, area, and spot ratio. It employs a quantitative, experimental design, manipulating image processing techniques to gauge their impact on classification accuracy. The results show the Naïve Bayes model achieved 95% accuracy, effectively distinguishing large and reject mangoes. It performed well for medium and small mangoes, with a 7% error rate between these classes. This indicates the model’s potential for quality control and sorting, though further refinement is needed to better differentiate between medium and small sizes. In conclusion, the study presents an image processing and Naïve Bayes-based method to classify carabao mangoes by size. The model’s high accuracy suggests its effectiveness and potential for automating mango classification, which could significantly aid the Philippine mango industry. Further performance assessment was conducted using a confusion matrix. The research highlights the promise of this approach for efficient mango grading.

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Section: Detailed Accuracy By Class In Terms Of Precision Recall and ...mentioning

confidence: 99%