Zha, Z.; Shi, D.; Chen, X.; Shi, H.; Wu, J. Classification of Appearance Quality of Red Grape Based on Transfer Learning of Convolution Neural Network. Agronomy2023, 13, 2015.
Zha, Z.; Shi, D.; Chen, X.; Shi, H.; Wu, J. Classification of Appearance Quality of Red Grape Based on Transfer Learning of Convolution Neural Network. Agronomy 2023, 13, 2015.
Zha, Z.; Shi, D.; Chen, X.; Shi, H.; Wu, J. Classification of Appearance Quality of Red Grape Based on Transfer Learning of Convolution Neural Network. Agronomy2023, 13, 2015.
Zha, Z.; Shi, D.; Chen, X.; Shi, H.; Wu, J. Classification of Appearance Quality of Red Grape Based on Transfer Learning of Convolution Neural Network. Agronomy 2023, 13, 2015.
Abstract
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). Initially, 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 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.
Keywords
grape; Appearance quality; Classification; Convolutional neural network; Transfer learning; Support vector machine
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
