Development of CNN-Based Semantic Segmentation Algorithm for Crop Classification of Korean Major Upland Crops Using NIA AI HUB
Accurately estimating crop cultivation areas is critical for predicting yields and managing overproduction, particularly for staple crops grown in regions like Jeju Island, South Korea, where reporting cultivation areas is mandatory. This study developed a modified U-Net architecture for semantic se...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10835106/ |
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| author | Dong-Wook Kim Gyujin Jang Hak-Jin Kim |
| author_facet | Dong-Wook Kim Gyujin Jang Hak-Jin Kim |
| author_sort | Dong-Wook Kim |
| collection | DOAJ |
| description | Accurately estimating crop cultivation areas is critical for predicting yields and managing overproduction, particularly for staple crops grown in regions like Jeju Island, South Korea, where reporting cultivation areas is mandatory. This study developed a modified U-Net architecture for semantic segmentation, utilizing UAV-based high-resolution imagery in the open-source NIA AI HUB dataset. The dataset includes labeled RGB images of six winter crops—white radish, cabbage, onion, garlic, broccoli, and carrot—grown on Jeju Island, a key agricultural hub. The proposed model incorporates a ResNet-34 backbone, Attention Gates, and Residual Modules, achieving a mean F1 score of 85.4% and an intersection over union (IoU) of 74.6%, outperforming the original U-Net. This advancement significantly reduces misclassifications among visually similar crops, such as garlic and onion. Application to three unknown fields demonstrated a mean prediction accuracy of 90.2%, effectively estimating cultivation areas with high precision. By leveraging public datasets and innovative AI techniques, this study highlights the scalability and practicality of the proposed model in enhancing precision agriculture. These findings demonstrate the model’s potential to improve crop yield prediction, optimize resource allocation, and support sustainable farming practices in diverse agricultural environments. |
| format | Article |
| id | doaj-art-9687d028a1254e71abcd58d2b99e262e |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-9687d028a1254e71abcd58d2b99e262e2025-01-21T00:02:27ZengIEEEIEEE Access2169-35362025-01-01138425843810.1109/ACCESS.2025.352750210835106Development of CNN-Based Semantic Segmentation Algorithm for Crop Classification of Korean Major Upland Crops Using NIA AI HUBDong-Wook Kim0https://orcid.org/0000-0001-5507-6617Gyujin Jang1https://orcid.org/0000-0001-8991-5765Hak-Jin Kim2https://orcid.org/0000-0001-6151-2446Department of Smart Farm Engineering, College of Industrial Sciences, Kongju National University, Yesan-gun, Chungcheongnam-do, Republic of KoreaIntegrated Major in Global Smart Farm, College of Agriculture and Life Sciences, Seoul National University, Seoul, Republic of KoreaIntegrated Major in Global Smart Farm, College of Agriculture and Life Sciences, Seoul National University, Seoul, Republic of KoreaAccurately estimating crop cultivation areas is critical for predicting yields and managing overproduction, particularly for staple crops grown in regions like Jeju Island, South Korea, where reporting cultivation areas is mandatory. This study developed a modified U-Net architecture for semantic segmentation, utilizing UAV-based high-resolution imagery in the open-source NIA AI HUB dataset. The dataset includes labeled RGB images of six winter crops—white radish, cabbage, onion, garlic, broccoli, and carrot—grown on Jeju Island, a key agricultural hub. The proposed model incorporates a ResNet-34 backbone, Attention Gates, and Residual Modules, achieving a mean F1 score of 85.4% and an intersection over union (IoU) of 74.6%, outperforming the original U-Net. This advancement significantly reduces misclassifications among visually similar crops, such as garlic and onion. Application to three unknown fields demonstrated a mean prediction accuracy of 90.2%, effectively estimating cultivation areas with high precision. By leveraging public datasets and innovative AI techniques, this study highlights the scalability and practicality of the proposed model in enhancing precision agriculture. These findings demonstrate the model’s potential to improve crop yield prediction, optimize resource allocation, and support sustainable farming practices in diverse agricultural environments.https://ieeexplore.ieee.org/document/10835106/Cultivation areaNIA AI HUBRGBsemantic segmentationUAV |
| spellingShingle | Dong-Wook Kim Gyujin Jang Hak-Jin Kim Development of CNN-Based Semantic Segmentation Algorithm for Crop Classification of Korean Major Upland Crops Using NIA AI HUB IEEE Access Cultivation area NIA AI HUB RGB semantic segmentation UAV |
| title | Development of CNN-Based Semantic Segmentation Algorithm for Crop Classification of Korean Major Upland Crops Using NIA AI HUB |
| title_full | Development of CNN-Based Semantic Segmentation Algorithm for Crop Classification of Korean Major Upland Crops Using NIA AI HUB |
| title_fullStr | Development of CNN-Based Semantic Segmentation Algorithm for Crop Classification of Korean Major Upland Crops Using NIA AI HUB |
| title_full_unstemmed | Development of CNN-Based Semantic Segmentation Algorithm for Crop Classification of Korean Major Upland Crops Using NIA AI HUB |
| title_short | Development of CNN-Based Semantic Segmentation Algorithm for Crop Classification of Korean Major Upland Crops Using NIA AI HUB |
| title_sort | development of cnn based semantic segmentation algorithm for crop classification of korean major upland crops using nia ai hub |
| topic | Cultivation area NIA AI HUB RGB semantic segmentation UAV |
| url | https://ieeexplore.ieee.org/document/10835106/ |
| work_keys_str_mv | AT dongwookkim developmentofcnnbasedsemanticsegmentationalgorithmforcropclassificationofkoreanmajoruplandcropsusingniaaihub AT gyujinjang developmentofcnnbasedsemanticsegmentationalgorithmforcropclassificationofkoreanmajoruplandcropsusingniaaihub AT hakjinkim developmentofcnnbasedsemanticsegmentationalgorithmforcropclassificationofkoreanmajoruplandcropsusingniaaihub |