Universite Cote d'Azur, LEAT, CNRS, 06903 Sophia Antipolis, France.
The University of Danang-University of Science and Technology, Danang 550000, Vietnam.
Sensors (Basel). 2023 Jan 13;23(2):952. doi: 10.3390/s23020952.
Fruit classification is required in many smart-farming and industrial applications. In the supermarket, a fruit classification system may be used to help cashiers and customer to identify the fruit species, origin, ripeness, and prices. Some methods, such as image processing and NIRS (near-infrared spectroscopy) are already used to classify fruit. In this paper, we propose a fast and cost-effective method based on a low-cost Vector Network Analyzer (VNA) device augmented by K-nearest neighbor (KNN) and Neural Network model. S-parameters features are selected, which take into account the information on signal amplitude or phase in the frequency domain, including reflection coefficient and transmission coefficient . This approach was experimentally tested for two separate datasets of five types of fruits, including Apple, Avocado, Dragon Fruit, Guava, and Mango, for fruit recognition as well as their level of ripeness. The classification accuracy of the Neural Network model was higher than KNN with 98.75% and 99.75% on the first dataset, whereas the KNN was seen to be more effective in classifying ripeness with 98.4% as compared to 96.6% for neural network.
水果分类在许多智慧农业和工业应用中是必需的。在超市中，水果分类系统可用于帮助收银员和顾客识别水果品种、来源、成熟度和价格。一些方法，如图像处理和近红外光谱（NIRS），已经被用于水果分类。在本文中，我们提出了一种基于低成本矢量网络分析仪（VNA）设备的快速且具有成本效益的方法，该方法通过 K-最近邻（KNN）和神经网络模型进行了增强。选择了 S 参数特征，这些特征考虑了频率域中信号幅度或相位的信息，包括反射系数和传输系数。该方法针对两个不同的数据集（包括苹果、鳄梨、火龙果、番石榴和芒果在内的五种水果）进行了实验测试，用于水果识别及其成熟度。在第一个数据集上，神经网络模型的分类准确率高于 KNN，达到 98.75%和 99.75%，而 KNN 在成熟度分类方面更为有效，准确率为 98.4%，而神经网络的准确率为 96.6%。
