Class distribution-aware adaptive margins and cluster embedding for classification of fruit and vegetables at supermarket self-checkouts

A novel method for vegetable and fruit classification based on using diffusion maps and machine learning

Vegetable and fruit classification can help all links of agricultural product circulation to better carry out inventory management, logistics planning and supply chain coordination, and improve the efficiency and response speed of the supply chain. However, the current classification of vegetables and fruits mainly relies on manual classification, which inevitably introduces the influence of human subjective factors, resulting in errors and misjudgments in the classification of vegetables and fruits. In response to this serious problem, this research proposes an efficient and reproducible novel model to classify multiple vegetables and fruits using handcrafted features. In the proposed model, preprocessing operations such as Gaussian filtering, grayscale and binarization are performed on the pictures of vegetables and fruits to improve the quality of the pictures; statistical texture features representing vegetable and fruit categories, wavelet transform features and shape features are extracted from the preprocessed images; the feature dimension reduction method of diffusion maps is used to reduce the redundant information of the combined features composed of statistical texture features, wavelet transform features and shape features; five effective machine learning methods were used to classify the types of vegetables and fruits. In this research, the proposed method was rigorously verified experimentally and the results show that the SVM classifier achieves 96.25% classification accuracy of vegetables and fruits, which proves that the proposed method is helpful to improve the quality and management level of vegetables and fruits, and provide strong support for agricultural production and supply chain.

Fruit-classification model resilience under adversarial attack

An accurate and robust fruit image classifier can have a variety of real-life and industrial applications including automated pricing, intelligent sorting, and information extraction. This paper demonstrates how adversarial training can enhance the robustness of fruit image classifiers. In the past, research in deep-learning-based fruit image classification has focused solely on attaining the highest possible accuracy of the model used in the classification process. However, even the highest accuracy models are still susceptible to adversarial attacks which pose serious problems for such systems in practice. As a robust fruit classifier can only be developed with the aid of a fruit image dataset consisting of fruit images photographed in realistic settings (rather than images taken in controlled laboratory settings), a new dataset of over three thousand fruit images belonging to seven fruit classes is presented. Each image is carefully selected so that its classification poses a significant challenge for the proposed classifiers. Three Convolutional Neural Network (CNN)-based classifiers are suggested: 1) IndusNet, 2) fine-tuned VGG16, and 3) fine-tuned MobileNet. Fine-tuned VGG16 produced the best test set accuracy of 94.82% compared to the 92.32% and the 94.28% produced by the other two models, respectively. Fine-tuned MobileNet has proved to be the most efficient model with a test time of 9 ms/step compared to the test times of 28 ms/step and 29 ms/step for the other two models. The empirical evidence presented demonstrates that adversarial training enables fruit image classifiers to resist attacks crafted through the Fast Gradient Sign Method (FGSM), while simultaneously improving classifiers’ robustness against other noise forms including ‘Gaussian’, ‘Salt and pepper’ and ‘Speckle’. For example, when the amplitude of the perturbations generated through the Fast Gradient Sign Method (FGSM) was kept at 0.1, adversarial training improved the fine-tuned VGG16’s performance on adversarial images by around 18% (i.e., from 76.6% to 94.82%), while simultaneously improving the classifier’s performance on fruit images corrupted with ‘salt and pepper’ noise by around 8% (i.e., from 69.82% to 77.85%). Other reported results also follow this pattern and demonstrate the effectiveness of adversarial training as a means of enhancing the robustness of fruit image classifiers.