Fruit classification is an important task in many industrial applications. A fruit classification system may be used to help a supermarket cashier identify the fruit species and prices. It may also be used to help people decide whether specific fruit species meet their dietary requirements. In this paper, we propose an efficient framework for fruit classification using deep learning. More specifically, the framework is based on two different deep learning architectures. The first is a proposed light model of six convolutional neural network layers, whereas the second is a fine-tuned visual geometry group-16 pretrained deep learning model. Two color image datasets, one of which is publicly available, are used to evaluate the proposed framework. The first dataset (dataset 1) consists of clear fruit images, whereas the second dataset (dataset 2) contains fruit images that are challenging to classify. Classification accuracies of 99.49% and 99.75% were achieved on dataset 1 for the first and second models, respectively. On dataset 2, the first and second models obtained accuracies of 85.43% and 96.75%, respectively.

中文翻译：

---

使用深度学习进行工业应用的自动水果分类

水果分类是许多工业应用中的重要任务。水果分类系统可用于帮助超市收银员识别水果的种类和价格。它也可以用来帮助人们确定特定的水果种类是否满足他们的饮食要求。在本文中，我们提出了一种使用深度学习进行水果分类的有效框架。更具体地说，该框架基于两种不同的深度学习架构。第一个是提出的六个卷积神经网络层的灯光模型，而第二个是微调的视觉几何体第16组预训练的深度学习模型。两个彩色图像数据集（其中一个是公开可用的）用于评估提出的框架。第一个数据集（数据集1）由清晰的水果图像组成，而第二个数据集（数据集2）包含难以分类的水果图像。在第一个模型和第二个模型的数据集1上分别实现了99.49％和99.75％的分类精度。在数据集2上，第一和第二个模型的准确度分别为85.43％和96.75％。