Segmentation and Fractional Coverage Estimation of Soil, Illuminated Vegetation, and Shaded Vegetation in Corn Canopy Images Using CCSNet and UAV Remote Sensing
The accurate estimation of corn canopy structure and light conditions is essential for effective crop management and informed variety selection. This study introduces CCSNet, a deep learning-based semantic segmentation model specifically developed to extract fractional coverages of soil, illuminated...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/15/12/1309 |
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| author | Shanxin Zhang Jibo Yue Xiaoyan Wang Haikuan Feng Yang Liu Meiyan Shu |
| author_facet | Shanxin Zhang Jibo Yue Xiaoyan Wang Haikuan Feng Yang Liu Meiyan Shu |
| author_sort | Shanxin Zhang |
| collection | DOAJ |
| description | The accurate estimation of corn canopy structure and light conditions is essential for effective crop management and informed variety selection. This study introduces CCSNet, a deep learning-based semantic segmentation model specifically developed to extract fractional coverages of soil, illuminated vegetation, and shaded vegetation from high-resolution corn canopy images acquired by UAVs. CCSNet improves segmentation accuracy by employing multi-level feature fusion and pyramid pooling to effectively capture multi-scale contextual information. The model was evaluated using Pixel Accuracy (PA), mean Intersection over Union (mIoU), and Recall, and was benchmarked against U-Net, PSPNet and UNetFormer. On the test set, CCSNet utilizing a ResNet50 backbone achieved the highest accuracy, with an mIoU of 86.42% and a PA of 93.58%. In addition, its estimation of fractional coverage for key canopy components yielded a root mean squared error (RMSE) ranging from 3.16% to 5.02%. Compared to lightweight backbones (e.g., MobileNetV2), CCSNet exhibited superior generalization performance when integrated with deeper backbones. These results highlight CCSNet’s capability to deliver high-precision segmentation and reliable phenotypic measurements. This provides valuable insights for breeders to evaluate light-use efficiency and facilitates intelligent decision-making in precision agriculture. |
| format | Article |
| id | doaj-art-d6c472dc30184d079736b2b4bd8fe24d |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-d6c472dc30184d079736b2b4bd8fe24d2025-08-20T03:26:20ZengMDPI AGAgriculture2077-04722025-06-011512130910.3390/agriculture15121309Segmentation and Fractional Coverage Estimation of Soil, Illuminated Vegetation, and Shaded Vegetation in Corn Canopy Images Using CCSNet and UAV Remote SensingShanxin Zhang0Jibo Yue1Xiaoyan Wang2Haikuan Feng3Yang Liu4Meiyan Shu5College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, ChinaCollege of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, ChinaChina Centre for Resources Satellite Data and Application, Beijing 100094, ChinaKey Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaKey Lab of Smart Agriculture System, Ministry of Education, China Agricultural University, Beijing 100083, ChinaCollege of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, ChinaThe accurate estimation of corn canopy structure and light conditions is essential for effective crop management and informed variety selection. This study introduces CCSNet, a deep learning-based semantic segmentation model specifically developed to extract fractional coverages of soil, illuminated vegetation, and shaded vegetation from high-resolution corn canopy images acquired by UAVs. CCSNet improves segmentation accuracy by employing multi-level feature fusion and pyramid pooling to effectively capture multi-scale contextual information. The model was evaluated using Pixel Accuracy (PA), mean Intersection over Union (mIoU), and Recall, and was benchmarked against U-Net, PSPNet and UNetFormer. On the test set, CCSNet utilizing a ResNet50 backbone achieved the highest accuracy, with an mIoU of 86.42% and a PA of 93.58%. In addition, its estimation of fractional coverage for key canopy components yielded a root mean squared error (RMSE) ranging from 3.16% to 5.02%. Compared to lightweight backbones (e.g., MobileNetV2), CCSNet exhibited superior generalization performance when integrated with deeper backbones. These results highlight CCSNet’s capability to deliver high-precision segmentation and reliable phenotypic measurements. This provides valuable insights for breeders to evaluate light-use efficiency and facilitates intelligent decision-making in precision agriculture.https://www.mdpi.com/2077-0472/15/12/1309segmentationdigital cameracorndeep learning |
| spellingShingle | Shanxin Zhang Jibo Yue Xiaoyan Wang Haikuan Feng Yang Liu Meiyan Shu Segmentation and Fractional Coverage Estimation of Soil, Illuminated Vegetation, and Shaded Vegetation in Corn Canopy Images Using CCSNet and UAV Remote Sensing Agriculture segmentation digital camera corn deep learning |
| title | Segmentation and Fractional Coverage Estimation of Soil, Illuminated Vegetation, and Shaded Vegetation in Corn Canopy Images Using CCSNet and UAV Remote Sensing |
| title_full | Segmentation and Fractional Coverage Estimation of Soil, Illuminated Vegetation, and Shaded Vegetation in Corn Canopy Images Using CCSNet and UAV Remote Sensing |
| title_fullStr | Segmentation and Fractional Coverage Estimation of Soil, Illuminated Vegetation, and Shaded Vegetation in Corn Canopy Images Using CCSNet and UAV Remote Sensing |
| title_full_unstemmed | Segmentation and Fractional Coverage Estimation of Soil, Illuminated Vegetation, and Shaded Vegetation in Corn Canopy Images Using CCSNet and UAV Remote Sensing |
| title_short | Segmentation and Fractional Coverage Estimation of Soil, Illuminated Vegetation, and Shaded Vegetation in Corn Canopy Images Using CCSNet and UAV Remote Sensing |
| title_sort | segmentation and fractional coverage estimation of soil illuminated vegetation and shaded vegetation in corn canopy images using ccsnet and uav remote sensing |
| topic | segmentation digital camera corn deep learning |
| url | https://www.mdpi.com/2077-0472/15/12/1309 |
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