Identifying Tomato Growth Stages in Protected Agriculture with StyleGAN3–Synthetic Images and Vision Transformer
In protected agriculture, accurately identifying the key growth stages of tomatoes plays a significant role in achieving efficient management and high-precision production. However, traditional approaches often face challenges like non-standardized data collection, unbalanced datasets, low recogniti...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/15/2/120 |
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| author | Yao Huo Yongbo Liu Peng He Liang Hu Wenbo Gao Le Gu |
| author_facet | Yao Huo Yongbo Liu Peng He Liang Hu Wenbo Gao Le Gu |
| author_sort | Yao Huo |
| collection | DOAJ |
| description | In protected agriculture, accurately identifying the key growth stages of tomatoes plays a significant role in achieving efficient management and high-precision production. However, traditional approaches often face challenges like non-standardized data collection, unbalanced datasets, low recognition efficiency, and limited accuracy. This paper proposes an innovative solution combining generative adversarial networks (GANs) and deep learning techniques to address these challenges. Specifically, the StyleGAN3 model is employed to generate high-quality images of tomato growth stages, effectively augmenting the original dataset with a broader range of images. This augmented dataset is then processed using a Vision Transformer (ViT) model for intelligent recognition of tomato growth stages within a protected agricultural environment. The proposed method was tested on 2723 images, demonstrating that the generated images are nearly indistinguishable from real images. The combined training approach incorporating both generated and original images produced superior recognition results compared to training with only the original images. The validation set achieved an accuracy of 99.6%, while the test set achieved 98.39%, marking improvements of 22.85%, 3.57%, and 3.21% over AlexNet, DenseNet50, and VGG16, respectively. The average detection speed was 9.5 ms. This method provides a highly effective means of identifying tomato growth stages in protected environments and offers valuable insights for improving the efficiency and quality of protected crop production. |
| format | Article |
| id | doaj-art-6b8a479c32124b0395d4d67afde2e65b |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-6b8a479c32124b0395d4d67afde2e65b2025-01-24T13:15:46ZengMDPI AGAgriculture2077-04722025-01-0115212010.3390/agriculture15020120Identifying Tomato Growth Stages in Protected Agriculture with StyleGAN3–Synthetic Images and Vision TransformerYao Huo0Yongbo Liu1Peng He2Liang Hu3Wenbo Gao4Le Gu5Institute of Agricultural Information and Rural Economy, Sichuan Academy of Agricultural Sciences, Chengdu 610011, ChinaInstitute of Agricultural Information and Rural Economy, Sichuan Academy of Agricultural Sciences, Chengdu 610011, ChinaInstitute of Agricultural Information and Rural Economy, Sichuan Academy of Agricultural Sciences, Chengdu 610011, ChinaInstitute of Agricultural Information and Rural Economy, Sichuan Academy of Agricultural Sciences, Chengdu 610011, ChinaInstitute of Agricultural Information and Rural Economy, Sichuan Academy of Agricultural Sciences, Chengdu 610011, ChinaInstitute of Agricultural Information and Rural Economy, Sichuan Academy of Agricultural Sciences, Chengdu 610011, ChinaIn protected agriculture, accurately identifying the key growth stages of tomatoes plays a significant role in achieving efficient management and high-precision production. However, traditional approaches often face challenges like non-standardized data collection, unbalanced datasets, low recognition efficiency, and limited accuracy. This paper proposes an innovative solution combining generative adversarial networks (GANs) and deep learning techniques to address these challenges. Specifically, the StyleGAN3 model is employed to generate high-quality images of tomato growth stages, effectively augmenting the original dataset with a broader range of images. This augmented dataset is then processed using a Vision Transformer (ViT) model for intelligent recognition of tomato growth stages within a protected agricultural environment. The proposed method was tested on 2723 images, demonstrating that the generated images are nearly indistinguishable from real images. The combined training approach incorporating both generated and original images produced superior recognition results compared to training with only the original images. The validation set achieved an accuracy of 99.6%, while the test set achieved 98.39%, marking improvements of 22.85%, 3.57%, and 3.21% over AlexNet, DenseNet50, and VGG16, respectively. The average detection speed was 9.5 ms. This method provides a highly effective means of identifying tomato growth stages in protected environments and offers valuable insights for improving the efficiency and quality of protected crop production.https://www.mdpi.com/2077-0472/15/2/120StyleGAN3ViTdeep learningtomato |
| spellingShingle | Yao Huo Yongbo Liu Peng He Liang Hu Wenbo Gao Le Gu Identifying Tomato Growth Stages in Protected Agriculture with StyleGAN3–Synthetic Images and Vision Transformer Agriculture StyleGAN3 ViT deep learning tomato |
| title | Identifying Tomato Growth Stages in Protected Agriculture with StyleGAN3–Synthetic Images and Vision Transformer |
| title_full | Identifying Tomato Growth Stages in Protected Agriculture with StyleGAN3–Synthetic Images and Vision Transformer |
| title_fullStr | Identifying Tomato Growth Stages in Protected Agriculture with StyleGAN3–Synthetic Images and Vision Transformer |
| title_full_unstemmed | Identifying Tomato Growth Stages in Protected Agriculture with StyleGAN3–Synthetic Images and Vision Transformer |
| title_short | Identifying Tomato Growth Stages in Protected Agriculture with StyleGAN3–Synthetic Images and Vision Transformer |
| title_sort | identifying tomato growth stages in protected agriculture with stylegan3 synthetic images and vision transformer |
| topic | StyleGAN3 ViT deep learning tomato |
| url | https://www.mdpi.com/2077-0472/15/2/120 |
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