Research on Maize Seed Classification and Recognition Based on Machine Vision and Deep Learning
Maize is one of the essential crops for food supply. Accurate sorting of seeds is critical for cultivation and marketing purposes, while the traditional methods of variety identification are time-consuming, inefficient, and easily damaged. This study proposes a rapid classification method for maize...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/12/2/232 |
| _version_ | 1797483797214658560 |
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| author | Peng Xu Qian Tan Yunpeng Zhang Xiantao Zha Songmei Yang Ranbing Yang |
| author_facet | Peng Xu Qian Tan Yunpeng Zhang Xiantao Zha Songmei Yang Ranbing Yang |
| author_sort | Peng Xu |
| collection | DOAJ |
| description | Maize is one of the essential crops for food supply. Accurate sorting of seeds is critical for cultivation and marketing purposes, while the traditional methods of variety identification are time-consuming, inefficient, and easily damaged. This study proposes a rapid classification method for maize seeds using a combination of machine vision and deep learning. 8080 maize seeds of five varieties were collected, and then the sample images were classified into training and validation sets in the proportion of 8:2, and the data were enhanced. The proposed improved network architecture, namely P-ResNet, was fine-tuned for transfer learning to recognize and categorize maize seeds, and then it compares the performance of the models. The results show that the overall classification accuracy was determined as 97.91, 96.44, 99.70, 97.84, 98.58, 97.13, 96.59, and 98.28% for AlexNet, VGGNet, P-ResNet, GoogLeNet, MobileNet, DenseNet, ShuffleNet, and EfficientNet, respectively. The highest classification accuracy result was obtained with P-ResNet, and the model loss remained at around 0.01. This model obtained the accuracy of classifications for BaoQiu, ShanCu, XinNuo, LiaoGe, and KouXian varieties, which reached 99.74, 99.68, 99.68, 99.61, and 99.80%, respectively. The experimental results demonstrated that the convolutional neural network model proposed enables the effective classification of maize seeds. It can provide a reference for identifying seeds of other crops and be applied to consumer use and the food industry. |
| first_indexed | 2024-03-09T22:52:13Z |
| format | Article |
| id | doaj.art-4f1e47453a66471088038480816deae7 |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-09T22:52:13Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-4f1e47453a66471088038480816deae72023-11-23T18:16:49ZengMDPI AGAgriculture2077-04722022-02-0112223210.3390/agriculture12020232Research on Maize Seed Classification and Recognition Based on Machine Vision and Deep LearningPeng Xu0Qian Tan1Yunpeng Zhang2Xiantao Zha3Songmei Yang4Ranbing Yang5College of Information and Communication Engineering, Hainan University, Haikou 570228, ChinaCollege of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, ChinaCollege of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, ChinaCollege of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, ChinaCollege of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, ChinaCollege of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, ChinaMaize is one of the essential crops for food supply. Accurate sorting of seeds is critical for cultivation and marketing purposes, while the traditional methods of variety identification are time-consuming, inefficient, and easily damaged. This study proposes a rapid classification method for maize seeds using a combination of machine vision and deep learning. 8080 maize seeds of five varieties were collected, and then the sample images were classified into training and validation sets in the proportion of 8:2, and the data were enhanced. The proposed improved network architecture, namely P-ResNet, was fine-tuned for transfer learning to recognize and categorize maize seeds, and then it compares the performance of the models. The results show that the overall classification accuracy was determined as 97.91, 96.44, 99.70, 97.84, 98.58, 97.13, 96.59, and 98.28% for AlexNet, VGGNet, P-ResNet, GoogLeNet, MobileNet, DenseNet, ShuffleNet, and EfficientNet, respectively. The highest classification accuracy result was obtained with P-ResNet, and the model loss remained at around 0.01. This model obtained the accuracy of classifications for BaoQiu, ShanCu, XinNuo, LiaoGe, and KouXian varieties, which reached 99.74, 99.68, 99.68, 99.61, and 99.80%, respectively. The experimental results demonstrated that the convolutional neural network model proposed enables the effective classification of maize seeds. It can provide a reference for identifying seeds of other crops and be applied to consumer use and the food industry.https://www.mdpi.com/2077-0472/12/2/232machine visionmaize seedsclassificationdeep learningconvolutional neural network |
| spellingShingle | Peng Xu Qian Tan Yunpeng Zhang Xiantao Zha Songmei Yang Ranbing Yang Research on Maize Seed Classification and Recognition Based on Machine Vision and Deep Learning Agriculture machine vision maize seeds classification deep learning convolutional neural network |
| title | Research on Maize Seed Classification and Recognition Based on Machine Vision and Deep Learning |
| title_full | Research on Maize Seed Classification and Recognition Based on Machine Vision and Deep Learning |
| title_fullStr | Research on Maize Seed Classification and Recognition Based on Machine Vision and Deep Learning |
| title_full_unstemmed | Research on Maize Seed Classification and Recognition Based on Machine Vision and Deep Learning |
| title_short | Research on Maize Seed Classification and Recognition Based on Machine Vision and Deep Learning |
| title_sort | research on maize seed classification and recognition based on machine vision and deep learning |
| topic | machine vision maize seeds classification deep learning convolutional neural network |
| url | https://www.mdpi.com/2077-0472/12/2/232 |
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