Improving crop image recognition performance using pseudolabels
In crop image recognition, when faced with a large quantity of unlabeled data, the traditional manual labeling method requires a large amount of human and material resources. To solve this problem, this study proposes an image recognition method based on a pseudolabeling technique. First, the data a...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Information Processing in Agriculture |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214317324000015 |
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| _version_ | 1849708325885706240 |
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| author | Pengfei Deng Zhaohui Jiang Huimin Ma Yuan Rao Wu Zhang |
| author_facet | Pengfei Deng Zhaohui Jiang Huimin Ma Yuan Rao Wu Zhang |
| author_sort | Pengfei Deng |
| collection | DOAJ |
| description | In crop image recognition, when faced with a large quantity of unlabeled data, the traditional manual labeling method requires a large amount of human and material resources. To solve this problem, this study proposes an image recognition method based on a pseudolabeling technique. First, the data are divided into labeled and unlabeled data. The initial network model is trained on labeled data. Then, pseudolabeling of the unlabeled data is predicted, and only the data that satisfy the confidence threshold are regarded as valid pseudolabeling. To convert the unlabeled data into supervised training data, the two types of data are mixed. The training is terminated when the number of remaining unlabeled data satisfies the end condition and when the fivefold cross-validation method is used to evaluate model performance. Compared with the traditional semisupervised method, the experimental method is simpler and more applicable. Experiments were conducted on rice growth stage recognition and crop weed seedling recognition tasks. The results showed that the proposed method achieved 99.17% accuracy in rice growth stage recognition and a high AUC value of 99.93% in crop weed seedling recognition, which demonstrated excellent performance. Compared with the traditional model, this method not only improves in accuracy but also has better stability and wider applicability and is expected to provide an efficient, accurate and scalable solution for crop image recognition. |
| format | Article |
| id | doaj-art-c8ef193c84b74b0582d76dd8afb660fc |
| institution | DOAJ |
| issn | 2214-3173 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Information Processing in Agriculture |
| spelling | doaj-art-c8ef193c84b74b0582d76dd8afb660fc2025-08-20T03:15:42ZengElsevierInformation Processing in Agriculture2214-31732025-03-01121172610.1016/j.inpa.2024.02.001Improving crop image recognition performance using pseudolabelsPengfei Deng0Zhaohui Jiang1Huimin Ma2Yuan Rao3Wu Zhang4School of Information & Computer, Anhui Agricultural University, Hefei 230036, ChinaCorresponding author.; School of Information & Computer, Anhui Agricultural University, Hefei 230036, ChinaSchool of Information & Computer, Anhui Agricultural University, Hefei 230036, ChinaSchool of Information & Computer, Anhui Agricultural University, Hefei 230036, ChinaSchool of Information & Computer, Anhui Agricultural University, Hefei 230036, ChinaIn crop image recognition, when faced with a large quantity of unlabeled data, the traditional manual labeling method requires a large amount of human and material resources. To solve this problem, this study proposes an image recognition method based on a pseudolabeling technique. First, the data are divided into labeled and unlabeled data. The initial network model is trained on labeled data. Then, pseudolabeling of the unlabeled data is predicted, and only the data that satisfy the confidence threshold are regarded as valid pseudolabeling. To convert the unlabeled data into supervised training data, the two types of data are mixed. The training is terminated when the number of remaining unlabeled data satisfies the end condition and when the fivefold cross-validation method is used to evaluate model performance. Compared with the traditional semisupervised method, the experimental method is simpler and more applicable. Experiments were conducted on rice growth stage recognition and crop weed seedling recognition tasks. The results showed that the proposed method achieved 99.17% accuracy in rice growth stage recognition and a high AUC value of 99.93% in crop weed seedling recognition, which demonstrated excellent performance. Compared with the traditional model, this method not only improves in accuracy but also has better stability and wider applicability and is expected to provide an efficient, accurate and scalable solution for crop image recognition.http://www.sciencedirect.com/science/article/pii/S2214317324000015CropsImage recognitionDeep learningTransfer learningPseudolabel |
| spellingShingle | Pengfei Deng Zhaohui Jiang Huimin Ma Yuan Rao Wu Zhang Improving crop image recognition performance using pseudolabels Information Processing in Agriculture Crops Image recognition Deep learning Transfer learning Pseudolabel |
| title | Improving crop image recognition performance using pseudolabels |
| title_full | Improving crop image recognition performance using pseudolabels |
| title_fullStr | Improving crop image recognition performance using pseudolabels |
| title_full_unstemmed | Improving crop image recognition performance using pseudolabels |
| title_short | Improving crop image recognition performance using pseudolabels |
| title_sort | improving crop image recognition performance using pseudolabels |
| topic | Crops Image recognition Deep learning Transfer learning Pseudolabel |
| url | http://www.sciencedirect.com/science/article/pii/S2214317324000015 |
| work_keys_str_mv | AT pengfeideng improvingcropimagerecognitionperformanceusingpseudolabels AT zhaohuijiang improvingcropimagerecognitionperformanceusingpseudolabels AT huiminma improvingcropimagerecognitionperformanceusingpseudolabels AT yuanrao improvingcropimagerecognitionperformanceusingpseudolabels AT wuzhang improvingcropimagerecognitionperformanceusingpseudolabels |