School of Mechanical Engineering, Shandong University, Jinan, 250061, Shandong, China.
Key Laboratory of High Efficiency and Clean Mechanical Manufacture of Ministry of Education, Shandong University, Jinan, 250061, Shandong, China.
Sci Rep. 2021 Aug 17;11(1):16618. doi: 10.1038/s41598-021-96103-2.
This work researched apple quality identification and classification from real images containing complicated disturbance information (background was similar to the surface of the apples). This paper proposed a novel model based on convolutional neural networks (CNN) which aimed at accurate and fast grading of apple quality. Specific, complex, and useful image characteristics for detection and classification were captured by the proposed model. Compared with existing methods, the proposed model could better learn high-order features of two adjacent layers that were not in the same channel but were very related. The proposed model was trained and validated, with best training and validation accuracy of 99% and 98.98% at 2590th and 3000th step, respectively. The overall accuracy of the proposed model tested using an independent 300 apple dataset was 95.33%. The results showed that the training accuracy, overall test accuracy and training time of the proposed model were better than Google Inception v3 model and traditional imaging process method based on histogram of oriented gradient (HOG), gray level co-occurrence matrix (GLCM) features merging and support vector machine (SVM) classifier. The proposed model has great potential in Apple's quality detection and classification.
本研究旨在从包含复杂干扰信息（背景与苹果表面相似）的实际图像中进行苹果质量识别与分类。本文提出了一种基于卷积神经网络（CNN）的新型模型，旨在实现苹果质量的精确和快速分级。具体来说，所提出的模型能够捕捉到用于检测和分类的复杂、有用的图像特征。与现有方法相比，该模型能够更好地学习两个相邻但相关性很强的非同一通道层之间的高阶特征。该模型经过训练和验证，在第 2590 步和第 3000 步的最佳训练和验证精度分别为 99%和 98.98%。使用独立的 300 个苹果数据集对所提出模型进行的总体准确性测试为 95.33%。结果表明，所提出模型的训练精度、总体测试精度和训练时间均优于 Google Inception v3 模型和基于方向梯度直方图（HOG）、灰度共生矩阵（GLCM）特征融合和支持向量机（SVM）分类器的传统成像处理方法。该模型在苹果质量检测和分类方面具有很大的潜力。
