Fruit variety recognition based on parallel convolutional neural network

doi:10.3969/j.issn.1004-1524.2022.11.22

Acta Agriculturae Zhejiangensis ›› 2022, Vol. 34 ›› Issue (11): 2533-2541.DOI: 10.3969/j.issn.1004-1524.2022.11.22

• Biosystems Engineering • Previous Articles Next Articles

Fruit variety recognition based on parallel convolutional neural network

LI Chao(), LI Feng(), HUANG Weijia

College of Electronic Information, Jiangsu University of Science and Technology, Zhenjiang 212000, Jiangsu, China

Received:2020-11-16 Online:2022-11-25 Published:2022-11-29
Contact: LI Feng

Abstract

Abstract:

In order to solve the defects of traditional fruit image recognition algorithms in feature extraction and the low recognition accuracy of traditional convolutional neural networks, a parallel convolutional neural network was proposed to extract fruit features. ELU activation function was introduced instead of ReLU activation function in the proposed model. Besides, a combination of maximum class spacing loss function and the traditional SoftmaxWithLoss loss function was designed to improve the recognition accuracy of similar varieties. The data of 8 fruit varieties in Fruit-360 data set was selected in the present study, and enhanced by the boundary equilibrium generative adversarial network (BEGAN) combined with the traditional data augmentation to generate a large number of high-quality data for model training. It was shown that the average recognition accuracy of 8 fruit varieties reached 98.85% and exhibited good recognition effect.

Key words: image recognition, deep learning, boundary equilibrium generative adversarial network, convolution neural network

CLC Number:

S126
TP391

LI Chao, LI Feng, HUANG Weijia. Fruit variety recognition based on parallel convolutional neural network[J]. Acta Agriculturae Zhejiangensis, 2022, 34(11): 2533-2541.

Figures/Tables 15

References 18

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类别 Category	传统数据增强 Traditional data enhancement	基于BEGAN的数据增强 Data enhancement via BEGAN
布瑞本苹果	93.25	95.51
Apple Braeburn
红雪苹果	93.40	95.49
Apple Crimson Snow
金色苹果2	93.26	96.13
Apple Golden2
金色苹果3	93.34	95.48
Apple Golden3
樱桃1 Cherry1	93.58	95.46
樱桃2 Cherry2	94.01	96.21
Rainer樱桃	94.12	95.45
Cherry Rainer
蜡黑樱桃	93.11	95.56
Cherry Wax Black

类别 Category	传统数据增强 Traditional data enhancement	基于BEGAN的数据增强 Data enhancement via BEGAN
布瑞本苹果	93.25	95.51
Apple Braeburn
红雪苹果	93.40	95.49
Apple Crimson Snow
金色苹果2	93.26	96.13
Apple Golden2
金色苹果3	93.34	95.48
Apple Golden3
樱桃1 Cherry1	93.58	95.46
樱桃2 Cherry2	94.01	96.21
Rainer樱桃	94.12	95.45
Cherry Rainer
蜡黑樱桃	93.11	95.56
Cherry Wax Black

类别 Category	旧损失函数 Old loss function	新损失函数 New loss function
布瑞本苹果Apple Braeburn	98.30	98.31
红雪苹果Apple Crimson Snow	97.59	99.50
金色苹果2 Apple Golden2	97.15	98.90
金色苹果3 Apple Golden3	97.80	97.85
樱桃1 Cherry1	98.41	99.45
樱桃2 Cherry2	97.73	98.79
Rainer樱桃Cherry Rainer	98.10	99.15
蜡黑樱桃Cherry Wax Black	97.86	98.86

类别 Category	旧损失函数 Old loss function	新损失函数 New loss function
布瑞本苹果Apple Braeburn	98.30	98.31
红雪苹果Apple Crimson Snow	97.59	99.50
金色苹果2 Apple Golden2	97.15	98.90
金色苹果3 Apple Golden3	97.80	97.85
樱桃1 Cherry1	98.41	99.45
樱桃2 Cherry2	97.73	98.79
Rainer樱桃Cherry Rainer	98.10	99.15
蜡黑樱桃Cherry Wax Black	97.86	98.86

模型 Model	准确率 Accuracy
舍去a通道的单只CNN Single CNN without channel a	97.10
特征加强+BP神经网络^[5] Feature enhancement+BP neural network	98.60
形状相似的水果自动识别研究^[11] Automatic recognition of fruits with similar shape	93.00
特征提取+单只CNN^[13] Feature extraction+single CNN	95.49
本文模型Proposed model	98.85

Fruit variety recognition based on parallel convolutional neural network

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