Research on Pepper External Quality Detection Based on Transfer Learning Integrated with Convolutional Neural Network
A pepper quality detection and classification model based on transfer learning combined with convolutional neural network is proposed as a solution for low efficiency of manual pepper sorting at the current stage. The pepper dataset was amplified with data pre-processing methods including rotation,...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/16/5305 |
| _version_ | 1797522161153343488 |
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| author | Rui Ren Shujuan Zhang Haixia Sun Tingyao Gao |
| author_facet | Rui Ren Shujuan Zhang Haixia Sun Tingyao Gao |
| author_sort | Rui Ren |
| collection | DOAJ |
| description | A pepper quality detection and classification model based on transfer learning combined with convolutional neural network is proposed as a solution for low efficiency of manual pepper sorting at the current stage. The pepper dataset was amplified with data pre-processing methods including rotation, luminance switch, and contrast ratio switch. To improve training speed and precision, a network model was optimized with a fine-tuned VGG 16 model in this research, transfer learning was applied after parameter optimization, and comparative analysis was performed by combining ResNet50, MobileNet V2, and GoogLeNet models. It turned out that the VGG 16 model output anticipation precision was 98.14%, and the prediction loss rate was 0.0669 when the dropout was settled as 0.3, learning rate settled as 0.000001, batch normalization added, and ReLU as activation function. Comparing with other finetune models and network models, this model was of better anticipation performance, as well as faster and more stable convergence rate, which embodied the best performance. Considering the basis of transfer learning and integration with strong generalization and fitting capacity of the VGG 16 finetune model, it is feasible to apply this model to the external quality classification of pepper, thus offering technical reference for further realizing the automatic classification of pepper quality. |
| first_indexed | 2024-03-10T08:24:33Z |
| format | Article |
| id | doaj.art-93d333bebb074f3eb104f0fcc301c589 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T08:24:33Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-93d333bebb074f3eb104f0fcc301c5892023-11-22T09:37:25ZengMDPI AGSensors1424-82202021-08-012116530510.3390/s21165305Research on Pepper External Quality Detection Based on Transfer Learning Integrated with Convolutional Neural NetworkRui Ren0Shujuan Zhang1Haixia Sun2Tingyao Gao3College of Agricultural Engineering, Shanxi Agriculture University, Jinzhong 030801, ChinaCollege of Agricultural Engineering, Shanxi Agriculture University, Jinzhong 030801, ChinaCollege of Agricultural Engineering, Shanxi Agriculture University, Jinzhong 030801, ChinaCollege of Agricultural Engineering, Shanxi Agriculture University, Jinzhong 030801, ChinaA pepper quality detection and classification model based on transfer learning combined with convolutional neural network is proposed as a solution for low efficiency of manual pepper sorting at the current stage. The pepper dataset was amplified with data pre-processing methods including rotation, luminance switch, and contrast ratio switch. To improve training speed and precision, a network model was optimized with a fine-tuned VGG 16 model in this research, transfer learning was applied after parameter optimization, and comparative analysis was performed by combining ResNet50, MobileNet V2, and GoogLeNet models. It turned out that the VGG 16 model output anticipation precision was 98.14%, and the prediction loss rate was 0.0669 when the dropout was settled as 0.3, learning rate settled as 0.000001, batch normalization added, and ReLU as activation function. Comparing with other finetune models and network models, this model was of better anticipation performance, as well as faster and more stable convergence rate, which embodied the best performance. Considering the basis of transfer learning and integration with strong generalization and fitting capacity of the VGG 16 finetune model, it is feasible to apply this model to the external quality classification of pepper, thus offering technical reference for further realizing the automatic classification of pepper quality.https://www.mdpi.com/1424-8220/21/16/5305pepperimage processingquality detectiontransfer learningconvolutional neural networkclassification |
| spellingShingle | Rui Ren Shujuan Zhang Haixia Sun Tingyao Gao Research on Pepper External Quality Detection Based on Transfer Learning Integrated with Convolutional Neural Network Sensors pepper image processing quality detection transfer learning convolutional neural network classification |
| title | Research on Pepper External Quality Detection Based on Transfer Learning Integrated with Convolutional Neural Network |
| title_full | Research on Pepper External Quality Detection Based on Transfer Learning Integrated with Convolutional Neural Network |
| title_fullStr | Research on Pepper External Quality Detection Based on Transfer Learning Integrated with Convolutional Neural Network |
| title_full_unstemmed | Research on Pepper External Quality Detection Based on Transfer Learning Integrated with Convolutional Neural Network |
| title_short | Research on Pepper External Quality Detection Based on Transfer Learning Integrated with Convolutional Neural Network |
| title_sort | research on pepper external quality detection based on transfer learning integrated with convolutional neural network |
| topic | pepper image processing quality detection transfer learning convolutional neural network classification |
| url | https://www.mdpi.com/1424-8220/21/16/5305 |
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