Segmenting Purple Rapeseed Leaves in the Field from UAV RGB Imagery Using Deep Learning as an Auxiliary Means for Nitrogen Stress Detection
Crop leaf purpling is a common phenotypic change when plants are subject to some biotic and abiotic stresses during their growth. The extraction of purple leaves can monitor crop stresses as an apparent trait and meanwhile contributes to crop phenotype analysis, monitoring, and yield estimation. Due...
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| Format: | Article |
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MDPI AG
2020-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/9/1403 |
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| author | Jian Zhang Tianjin Xie Chenghai Yang Huaibo Song Zhao Jiang Guangsheng Zhou Dongyan Zhang Hui Feng Jing Xie |
| author_facet | Jian Zhang Tianjin Xie Chenghai Yang Huaibo Song Zhao Jiang Guangsheng Zhou Dongyan Zhang Hui Feng Jing Xie |
| author_sort | Jian Zhang |
| collection | DOAJ |
| description | Crop leaf purpling is a common phenotypic change when plants are subject to some biotic and abiotic stresses during their growth. The extraction of purple leaves can monitor crop stresses as an apparent trait and meanwhile contributes to crop phenotype analysis, monitoring, and yield estimation. Due to the complexity of the field environment as well as differences in size, shape, texture, and color gradation among the leaves, purple leaf segmentation is difficult. In this study, we used a U-Net model for segmenting purple rapeseed leaves during the seedling stage based on unmanned aerial vehicle (UAV) RGB imagery at the pixel level. With the limited spatial resolution of rapeseed images acquired by UAV and small object size, the input patch size was carefully selected. Experiments showed that the U-Net model with the patch size of 256 × 256 pixels obtained better and more stable results with a F-measure of 90.29% and an Intersection of Union (IoU) of 82.41%. To further explore the influence of image spatial resolution, we evaluated the performance of the U-Net model with different image resolutions and patch sizes. The U-Net model performed better compared with four other commonly used image segmentation approaches comprising support vector machine, random forest, HSeg, and SegNet. Moreover, regression analysis was performed between the purple rapeseed leaf ratios and the measured N content. The negative exponential model had a coefficient of determination (R²) of 0.858, thereby explaining much of the rapeseed leaf purpling in this study. This purple leaf phenotype could be an auxiliary means for monitoring crop growth status so that crops could be managed in a timely and effective manner when nitrogen stress occurs. Results demonstrate that the U-Net model is a robust method for purple rapeseed leaf segmentation and that the accurate segmentation of purple leaves provides a new method for crop nitrogen stress monitoring. |
| first_indexed | 2024-03-10T20:09:53Z |
| format | Article |
| id | doaj.art-d5d07d4f46934f328e191abd6e49742f |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T20:09:53Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d5d07d4f46934f328e191abd6e49742f2023-11-19T23:01:23ZengMDPI AGRemote Sensing2072-42922020-04-01129140310.3390/rs12091403Segmenting Purple Rapeseed Leaves in the Field from UAV RGB Imagery Using Deep Learning as an Auxiliary Means for Nitrogen Stress DetectionJian Zhang0Tianjin Xie1Chenghai Yang2Huaibo Song3Zhao Jiang4Guangsheng Zhou5Dongyan Zhang6Hui Feng7Jing Xie8Macro Agriculture Research Institute, College of Resource and Environment, Huazhong Agricultural University, 1 Shizishan Street, Wuhan 430070, ChinaMacro Agriculture Research Institute, College of Resource and Environment, Huazhong Agricultural University, 1 Shizishan Street, Wuhan 430070, ChinaAerial Application Technology Research Unit, USDA-Agricultural Research Service, College Station, TX 77845, USACollege of Mechanical and Electronic Engineering, Northwest A&F University, 22 Xinong Road, Yangling 712100, ChinaMacro Agriculture Research Institute, College of Resource and Environment, Huazhong Agricultural University, 1 Shizishan Street, Wuhan 430070, ChinaCollege of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, ChinaAnhui Engineering Laboratory of Agro-Ecological Big Data, Anhui University, 111 Jiulong Road, Hefei 230601, ChinaCollege of Informatics, Huazhong Agricultural University, Wuhan 430070, ChinaCollege of Science, Huazhong Agricultural University, Wuhan 430070, ChinaCrop leaf purpling is a common phenotypic change when plants are subject to some biotic and abiotic stresses during their growth. The extraction of purple leaves can monitor crop stresses as an apparent trait and meanwhile contributes to crop phenotype analysis, monitoring, and yield estimation. Due to the complexity of the field environment as well as differences in size, shape, texture, and color gradation among the leaves, purple leaf segmentation is difficult. In this study, we used a U-Net model for segmenting purple rapeseed leaves during the seedling stage based on unmanned aerial vehicle (UAV) RGB imagery at the pixel level. With the limited spatial resolution of rapeseed images acquired by UAV and small object size, the input patch size was carefully selected. Experiments showed that the U-Net model with the patch size of 256 × 256 pixels obtained better and more stable results with a F-measure of 90.29% and an Intersection of Union (IoU) of 82.41%. To further explore the influence of image spatial resolution, we evaluated the performance of the U-Net model with different image resolutions and patch sizes. The U-Net model performed better compared with four other commonly used image segmentation approaches comprising support vector machine, random forest, HSeg, and SegNet. Moreover, regression analysis was performed between the purple rapeseed leaf ratios and the measured N content. The negative exponential model had a coefficient of determination (R²) of 0.858, thereby explaining much of the rapeseed leaf purpling in this study. This purple leaf phenotype could be an auxiliary means for monitoring crop growth status so that crops could be managed in a timely and effective manner when nitrogen stress occurs. Results demonstrate that the U-Net model is a robust method for purple rapeseed leaf segmentation and that the accurate segmentation of purple leaves provides a new method for crop nitrogen stress monitoring.https://www.mdpi.com/2072-4292/12/9/1403purple rapeseed leavesunmanned aerial vehicleU-Netplant segmentationnitrogen stress |
| spellingShingle | Jian Zhang Tianjin Xie Chenghai Yang Huaibo Song Zhao Jiang Guangsheng Zhou Dongyan Zhang Hui Feng Jing Xie Segmenting Purple Rapeseed Leaves in the Field from UAV RGB Imagery Using Deep Learning as an Auxiliary Means for Nitrogen Stress Detection Remote Sensing purple rapeseed leaves unmanned aerial vehicle U-Net plant segmentation nitrogen stress |
| title | Segmenting Purple Rapeseed Leaves in the Field from UAV RGB Imagery Using Deep Learning as an Auxiliary Means for Nitrogen Stress Detection |
| title_full | Segmenting Purple Rapeseed Leaves in the Field from UAV RGB Imagery Using Deep Learning as an Auxiliary Means for Nitrogen Stress Detection |
| title_fullStr | Segmenting Purple Rapeseed Leaves in the Field from UAV RGB Imagery Using Deep Learning as an Auxiliary Means for Nitrogen Stress Detection |
| title_full_unstemmed | Segmenting Purple Rapeseed Leaves in the Field from UAV RGB Imagery Using Deep Learning as an Auxiliary Means for Nitrogen Stress Detection |
| title_short | Segmenting Purple Rapeseed Leaves in the Field from UAV RGB Imagery Using Deep Learning as an Auxiliary Means for Nitrogen Stress Detection |
| title_sort | segmenting purple rapeseed leaves in the field from uav rgb imagery using deep learning as an auxiliary means for nitrogen stress detection |
| topic | purple rapeseed leaves unmanned aerial vehicle U-Net plant segmentation nitrogen stress |
| url | https://www.mdpi.com/2072-4292/12/9/1403 |
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