Tomato Quality Classification Based on Transfer Learning Feature Extraction and Machine Learning Algorithm Classifiers

Tomato Quality Classification Based on Transfer Learning Feature Extraction and Machine Learning Algorithm Classifiers

Blog Article

The demand for high-quality tomatoes to meet consumer and market standards, combined with large-scale production, has necessitated the development of an inline quality grading.Since manual grading is time-consuming, costly, and requires a substantial amount of labor.This study introduces a novel approach for tomato quality sorting and grading.The method leverages pre-trained convolutional neural networks (CNNs) for feature extraction and traditional machine-learning algorithms for classification (hybrid model).

The single-board computer NVIDIA Jetson TX1 was Makeup Set used to create a tomato image dataset.Image preprocessing and fine-tuning techniques were applied to enable deep layers to learn and concentrate on complex and significant features.The extracted features were then classified using traditional machine learning algorithms namely: support vector machines (SVM), random forest (RF), and k-nearest neighbors (KNN) classifiers.Among the proposed hybrid models, the CNN-SVM method has outperformed other hybrid approaches, attaining an accuracy of 97.

50% in the Towels binary classification of tomatoes as healthy or rejected and 96.67% in the multiclass classification of them as ripe, unripe, or rejected when Inceptionv3 was used as feature extractor.Once another dataset (public dataset) was used, the proposed hybrid model CNN-SVM achieved an accuracy of 97.54% in categorizing tomatoes as ripe, unripe, old, or damaged outperforming other hybrid models when Inceptionv3 was used as a feature extractor.

The performance metrics accuracy, recall, precision, specificity, and F1-score of the best-performing proposed hybrid model were evaluated.