文献类型： 外文期刊

作者： Zha, Zhihua ¹ ; Shi, Dongyuan ² ; Chen, Xiaohui ³ ; Shi, Hui ¹ ; Wu, Jie ¹ ;

作者机构： 1.Shihezi Univ, Coll Mech & Elect Engn, Shihezi 832003, Peoples R China

2.Shihezi Univ, Dept Hort, Agr Coll, Shihezi 832003, Peoples R China

3.China Meteorol Adm Agr & Rural Affairs, Natl Engn Res Ctr Informat Technol Agr, Res Ctr Informat Technol,Meteorol Serv Ctr Urban, Natl Engn Lab AgriProd Qual Traceability,Beijing, Beijing 100097, Peoples R China

4.Minist Agr & Rural Affairs, Key Lab Northwest Agr Equipment, Shihezi 832003, Peoples R China

5.Minist Educ, Engn Res Ctr Prod Mech Oasis Characterist Cash Cr, Shihezi 832003, Peoples R China

关键词： grape; appearance quality; classification; convolutional neural network; transfer learning; support vector machine

期刊名称：AGRONOMY-BASEL （ 影响因子：3.7； 五年影响因子：4.0 ）

ISSN：

年卷期： 2023 年 13 卷 8 期

页码：

收录情况： SCI

摘要： Grapes are a globally popular fruit, with grape cultivation worldwide being second only to citrus. This article focuses on the low efficiency and accuracy of traditional manual grading of red grape external appearance and proposes a small-sample red grape external appearance grading model based on transfer learning with convolutional neural networks (CNNs). In the naturally growing vineyards, 195,120,135 samples of Grade I, Grade II, and Grade III red grapes were collected using a Canon EOS 550D camera, and a data set of 1800 samples was obtained using data enhancement technology. Then, the CNN transfer learning method was used to transfer the pre-trained AlexNet, VGG16, GoogleNet, InceptionV3, and ResNet50 network models on the ImageNet image dataset to the red grape image grading task. By comparing the classification performance of the CNN models of these five different network depths with fine-tuning, ResNet50 with a learning rate of 0.001 and a loop number of 10 was determined to be the best feature extractor for red grape images. Moreover, given the small number of red grape image samples in this study, different convolutional layer features output by the ResNet50 feature extractor were analyzed layer by layer to determine the effect of deep features extracted by each convolutional layer on Support Vector Machine (SVM) classification performance. This analysis helped to obtain a ResNet50 + SVM red grape external appearance grading model based on the optimal ResNet50 feature extraction strategy. Experimental data showed that the classification model constructed using the feature parameters extracted from the 10th node of the ResNet50 network achieved an accuracy rate of 95.08% for red grape grading. These research results provide a reference for the online grading of red grape clusters based on external appearance quality and have certain guiding significance for the quality and efficiency of grape industry circulation and production.