Identification of Cotton Root Rot by Multifeature Selection from Sentinel-2 Images Using Random Forest
Cotton root rot is a destructive cotton disease and significantly affects cotton quality and yield, and accurate identification of its distribution within fields is critical for cotton growers to control the disease effectively. In this study, Sentinel-2 images were used to explore the feasibility o...
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| Format: | Article |
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MDPI AG
2020-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/21/3504 |
| _version_ | 1797549909542436864 |
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| author | Xingrong Li Chenghai Yang Wenjiang Huang Jia Tang Yanqin Tian Qing Zhang |
| author_facet | Xingrong Li Chenghai Yang Wenjiang Huang Jia Tang Yanqin Tian Qing Zhang |
| author_sort | Xingrong Li |
| collection | DOAJ |
| description | Cotton root rot is a destructive cotton disease and significantly affects cotton quality and yield, and accurate identification of its distribution within fields is critical for cotton growers to control the disease effectively. In this study, Sentinel-2 images were used to explore the feasibility of creating classification maps and prescription maps for site-specific fungicide application. Eight cotton fields with different levels of root rot were selected and random forest (RF) was used to identify the optimal spectral indices and texture features of the Sentinel-2 images. Five optimal spectral indices (plant senescence reflectance index (PSRI), normalized difference vegetation index (NDVI), normalized difference water index (NDWI1), moisture stressed index (MSI), and renormalized difference vegetation index (RDVI)) and seven optimal texture features (Contrast 1, Dissimilarity 1, Entory 2, Mean 1, Variance 1, Homogeneity 1, and Second moment 2) were identified. Three binary logistic regression (BLR) models, including a spectral model, a texture model, and a spectral-texture model, were constructed for cotton root rot classification and prescription map creation. The results were compared with classification maps and prescription maps based on airborne imagery. Accuracy assessment showed that the accuracies of the classification maps for the spectral, texture, and spectral-texture models were 92.95%, 84.81%, and 91.87%, respectively, and the accuracies of the prescription maps for the three respective models were 90.83%, 87.14%, and 91.40%. These results confirmed that it was feasible to identify cotton root rot and create prescription maps using different features of Sentinel-2 imagery. The addition of texture features had little effect on the overall accuracy, but it could improve the ability to identify root rot areas. The producer’s accuracy (PA) for infested cotton in the classification maps for the texture model and the spectral-texture model was 2.82% and 1.07% higher, respectively, than that of the spectral model, and the PA for treatment zones in the prescription maps for the two respective models was 8.6% and 8.22% higher than that of the spectral model. Results based on the eight cotton fields showed that the spectral model was appropriate for the cotton fields with relatively severe infestation and the spectral-texture model was more appropriate for the cotton fields with low or moderate infestation. |
| first_indexed | 2024-03-10T15:21:05Z |
| format | Article |
| id | doaj.art-cce42e1c62af4ad0b919f09689c12568 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T15:21:05Z |
| publishDate | 2020-10-01 |
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| spelling | doaj.art-cce42e1c62af4ad0b919f09689c125682023-11-20T18:28:26ZengMDPI AGRemote Sensing2072-42922020-10-011221350410.3390/rs12213504Identification of Cotton Root Rot by Multifeature Selection from Sentinel-2 Images Using Random ForestXingrong Li0Chenghai Yang1Wenjiang Huang2Jia Tang3Yanqin Tian4Qing Zhang5Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerial Application Technology Research Unit, USDA-Agricultural Research Service, 3103 F and B Road, College Station, TX 77845, USAKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaFaculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaCotton root rot is a destructive cotton disease and significantly affects cotton quality and yield, and accurate identification of its distribution within fields is critical for cotton growers to control the disease effectively. In this study, Sentinel-2 images were used to explore the feasibility of creating classification maps and prescription maps for site-specific fungicide application. Eight cotton fields with different levels of root rot were selected and random forest (RF) was used to identify the optimal spectral indices and texture features of the Sentinel-2 images. Five optimal spectral indices (plant senescence reflectance index (PSRI), normalized difference vegetation index (NDVI), normalized difference water index (NDWI1), moisture stressed index (MSI), and renormalized difference vegetation index (RDVI)) and seven optimal texture features (Contrast 1, Dissimilarity 1, Entory 2, Mean 1, Variance 1, Homogeneity 1, and Second moment 2) were identified. Three binary logistic regression (BLR) models, including a spectral model, a texture model, and a spectral-texture model, were constructed for cotton root rot classification and prescription map creation. The results were compared with classification maps and prescription maps based on airborne imagery. Accuracy assessment showed that the accuracies of the classification maps for the spectral, texture, and spectral-texture models were 92.95%, 84.81%, and 91.87%, respectively, and the accuracies of the prescription maps for the three respective models were 90.83%, 87.14%, and 91.40%. These results confirmed that it was feasible to identify cotton root rot and create prescription maps using different features of Sentinel-2 imagery. The addition of texture features had little effect on the overall accuracy, but it could improve the ability to identify root rot areas. The producer’s accuracy (PA) for infested cotton in the classification maps for the texture model and the spectral-texture model was 2.82% and 1.07% higher, respectively, than that of the spectral model, and the PA for treatment zones in the prescription maps for the two respective models was 8.6% and 8.22% higher than that of the spectral model. Results based on the eight cotton fields showed that the spectral model was appropriate for the cotton fields with relatively severe infestation and the spectral-texture model was more appropriate for the cotton fields with low or moderate infestation.https://www.mdpi.com/2072-4292/12/21/3504cotton root rotSentinel-2random forestbinary logistic regressionspectral and texture analysisairborne multispectral imagery |
| spellingShingle | Xingrong Li Chenghai Yang Wenjiang Huang Jia Tang Yanqin Tian Qing Zhang Identification of Cotton Root Rot by Multifeature Selection from Sentinel-2 Images Using Random Forest Remote Sensing cotton root rot Sentinel-2 random forest binary logistic regression spectral and texture analysis airborne multispectral imagery |
| title | Identification of Cotton Root Rot by Multifeature Selection from Sentinel-2 Images Using Random Forest |
| title_full | Identification of Cotton Root Rot by Multifeature Selection from Sentinel-2 Images Using Random Forest |
| title_fullStr | Identification of Cotton Root Rot by Multifeature Selection from Sentinel-2 Images Using Random Forest |
| title_full_unstemmed | Identification of Cotton Root Rot by Multifeature Selection from Sentinel-2 Images Using Random Forest |
| title_short | Identification of Cotton Root Rot by Multifeature Selection from Sentinel-2 Images Using Random Forest |
| title_sort | identification of cotton root rot by multifeature selection from sentinel 2 images using random forest |
| topic | cotton root rot Sentinel-2 random forest binary logistic regression spectral and texture analysis airborne multispectral imagery |
| url | https://www.mdpi.com/2072-4292/12/21/3504 |
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