Detecting the Early Stage of Phaeosphaeria Leaf Spot Infestations in Maize Crop Using In Situ Hyperspectral Data and Guided Regularized Random Forest Algorithm
Phaeosphaeria leaf spot (PLS) is considered one of the major diseases that threaten the stability of maize production in tropical and subtropical African regions. The objective of the present study was to investigate the use of hyperspectral data in detecting the early stage of PLS in tropical maize...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-01-01
|
| Series: | Journal of Spectroscopy |
| Online Access: | http://dx.doi.org/10.1155/2017/6961387 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832565374254579712 |
|---|---|
| author | Elhadi Adam Houtao Deng John Odindi Elfatih M. Abdel-Rahman Onisimo Mutanga |
| author_facet | Elhadi Adam Houtao Deng John Odindi Elfatih M. Abdel-Rahman Onisimo Mutanga |
| author_sort | Elhadi Adam |
| collection | DOAJ |
| description | Phaeosphaeria leaf spot (PLS) is considered one of the major diseases that threaten the stability of maize production in tropical and subtropical African regions. The objective of the present study was to investigate the use of hyperspectral data in detecting the early stage of PLS in tropical maize. Field data were collected from healthy and the early stage of PLS over two years (2013 and 2014) using a handheld spectroradiometer. An integration of a newly developed guided regularized random forest (GRRF) and a traditional random forest (RF) was used for feature selection and classification, respectively. The 2013 dataset was used to train the model, while the 2014 dataset was used as independent test dataset. Results showed that there were statistically significant differences in biochemical concentration between the healthy leaves and leaves that were at an early stage of PLS infestation. The newly developed GRRF was able to reduce the high dimensionality of hyperspectral data by selecting key wavelengths with less autocorrelation. These wavelengths are located at 420 nm, 795 nm, 779 nm, 1543 nm, 1747 nm, and 1010 nm. Using these variables (n=6), a random forest classifier was able to discriminate between the healthy maize and maize at an early stage of PLS infestation with an overall accuracy of 88% and a kappa value of 0.75. Overall, our study showed potential application of hyperspectral data, GRRF feature selection, and RF classifiers in detecting the early stage of PLS infestation in tropical maize. |
| format | Article |
| id | doaj-art-3bff9bad803a478caf676d0d03aaf28f |
| institution | Kabale University |
| issn | 2314-4920 2314-4939 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Spectroscopy |
| spelling | doaj-art-3bff9bad803a478caf676d0d03aaf28f2025-02-03T01:07:53ZengWileyJournal of Spectroscopy2314-49202314-49392017-01-01201710.1155/2017/69613876961387Detecting the Early Stage of Phaeosphaeria Leaf Spot Infestations in Maize Crop Using In Situ Hyperspectral Data and Guided Regularized Random Forest AlgorithmElhadi Adam0Houtao Deng1John Odindi2Elfatih M. Abdel-Rahman3Onisimo Mutanga4School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Private Bag 3, Wits, Johannesburg 2050, South AfricaIntuit Mountain View, Mountain View, CA, USASchool of Agriculture, Earth and Environmental Sciences, Pietermaritzburg Campus, University of KwaZulu-Natal, Scottsville P/Bag X01, Pietermaritzburg 3209, South AfricaSchool of Agriculture, Earth and Environmental Sciences, Pietermaritzburg Campus, University of KwaZulu-Natal, Scottsville P/Bag X01, Pietermaritzburg 3209, South AfricaSchool of Agriculture, Earth and Environmental Sciences, Pietermaritzburg Campus, University of KwaZulu-Natal, Scottsville P/Bag X01, Pietermaritzburg 3209, South AfricaPhaeosphaeria leaf spot (PLS) is considered one of the major diseases that threaten the stability of maize production in tropical and subtropical African regions. The objective of the present study was to investigate the use of hyperspectral data in detecting the early stage of PLS in tropical maize. Field data were collected from healthy and the early stage of PLS over two years (2013 and 2014) using a handheld spectroradiometer. An integration of a newly developed guided regularized random forest (GRRF) and a traditional random forest (RF) was used for feature selection and classification, respectively. The 2013 dataset was used to train the model, while the 2014 dataset was used as independent test dataset. Results showed that there were statistically significant differences in biochemical concentration between the healthy leaves and leaves that were at an early stage of PLS infestation. The newly developed GRRF was able to reduce the high dimensionality of hyperspectral data by selecting key wavelengths with less autocorrelation. These wavelengths are located at 420 nm, 795 nm, 779 nm, 1543 nm, 1747 nm, and 1010 nm. Using these variables (n=6), a random forest classifier was able to discriminate between the healthy maize and maize at an early stage of PLS infestation with an overall accuracy of 88% and a kappa value of 0.75. Overall, our study showed potential application of hyperspectral data, GRRF feature selection, and RF classifiers in detecting the early stage of PLS infestation in tropical maize.http://dx.doi.org/10.1155/2017/6961387 |
| spellingShingle | Elhadi Adam Houtao Deng John Odindi Elfatih M. Abdel-Rahman Onisimo Mutanga Detecting the Early Stage of Phaeosphaeria Leaf Spot Infestations in Maize Crop Using In Situ Hyperspectral Data and Guided Regularized Random Forest Algorithm Journal of Spectroscopy |
| title | Detecting the Early Stage of Phaeosphaeria Leaf Spot Infestations in Maize Crop Using In Situ Hyperspectral Data and Guided Regularized Random Forest Algorithm |
| title_full | Detecting the Early Stage of Phaeosphaeria Leaf Spot Infestations in Maize Crop Using In Situ Hyperspectral Data and Guided Regularized Random Forest Algorithm |
| title_fullStr | Detecting the Early Stage of Phaeosphaeria Leaf Spot Infestations in Maize Crop Using In Situ Hyperspectral Data and Guided Regularized Random Forest Algorithm |
| title_full_unstemmed | Detecting the Early Stage of Phaeosphaeria Leaf Spot Infestations in Maize Crop Using In Situ Hyperspectral Data and Guided Regularized Random Forest Algorithm |
| title_short | Detecting the Early Stage of Phaeosphaeria Leaf Spot Infestations in Maize Crop Using In Situ Hyperspectral Data and Guided Regularized Random Forest Algorithm |
| title_sort | detecting the early stage of phaeosphaeria leaf spot infestations in maize crop using in situ hyperspectral data and guided regularized random forest algorithm |
| url | http://dx.doi.org/10.1155/2017/6961387 |
| work_keys_str_mv | AT elhadiadam detectingtheearlystageofphaeosphaerialeafspotinfestationsinmaizecropusinginsituhyperspectraldataandguidedregularizedrandomforestalgorithm AT houtaodeng detectingtheearlystageofphaeosphaerialeafspotinfestationsinmaizecropusinginsituhyperspectraldataandguidedregularizedrandomforestalgorithm AT johnodindi detectingtheearlystageofphaeosphaerialeafspotinfestationsinmaizecropusinginsituhyperspectraldataandguidedregularizedrandomforestalgorithm AT elfatihmabdelrahman detectingtheearlystageofphaeosphaerialeafspotinfestationsinmaizecropusinginsituhyperspectraldataandguidedregularizedrandomforestalgorithm AT onisimomutanga detectingtheearlystageofphaeosphaerialeafspotinfestationsinmaizecropusinginsituhyperspectraldataandguidedregularizedrandomforestalgorithm |