Development of CNN-Based Semantic Segmentation Algorithm for Crop Classification of Korean Major Upland Crops Using NIA AI HUB

被引:0
作者
Kim, Dong-Wook [1 ]
Jang, Gyujin [2 ,3 ]
Kim, Hak-Jin [2 ,3 ,4 ,5 ]
机构
[1] Kongju Natl Univ, Coll Ind Sci, Dept Smart Farm Engn, Yesan Gun 32439, Chungcheongnam, South Korea
[2] Seoul Natl Univ, Coll Agr & Life Sci, Integrated Major Global Smart Farm, Seoul 08826, South Korea
[3] Seoul Natl Univ, Coll Agr & Life Sci, Dept Biosyst Engn, Seoul 08826, South Korea
[4] Seoul Natl Univ, Res Inst Agr & Life Sci, Coll Agr & Life Sci, Seoul 08826, South Korea
[5] Adv Inst Convergence Technol, Suwon 16229, South Korea
关键词
Crops; Accuracy; Artificial intelligence; Agriculture; Deep learning; Autonomous aerial vehicles; Semantic segmentation; Hyperspectral imaging; Classification algorithms; Predictive models; Cultivation area; NIA AI HUB; RGB; semantic segmentation; UAV; COVER; TIME;
D O I
10.1109/ACCESS.2025.3527502
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:8425 / 8438
页数:14
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