Remote sensing of rice leaf folder damage using ground-based hyperspectral radiometry
Rice is the staple food for more than 60 % of the world's population and pest damage is one of the major limiting factors of rice production in India. Rice leaf folder, Cnaphalocrocis medinalis (Guenee) (Lepidoptera: Pyralidae) is assuming the major pest status in view of its severe damage. The...
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| Language: | English |
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Elsevier
2025-03-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375524003617 |
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| author | Mathyam Prabhakar Ch Padmavathi Merugu Thirupathi Srasvan Kumar Golla Uppu Sai Sravan G. Ramachandra Rao Madduri Kalpana Vallabuni Sailaja Pebbeti Chandana Yenumula G. Prasad M. Srinivasa Rao V.K. Singh Rajbir Singh |
| author_facet | Mathyam Prabhakar Ch Padmavathi Merugu Thirupathi Srasvan Kumar Golla Uppu Sai Sravan G. Ramachandra Rao Madduri Kalpana Vallabuni Sailaja Pebbeti Chandana Yenumula G. Prasad M. Srinivasa Rao V.K. Singh Rajbir Singh |
| author_sort | Mathyam Prabhakar |
| collection | DOAJ |
| description | Rice is the staple food for more than 60 % of the world's population and pest damage is one of the major limiting factors of rice production in India. Rice leaf folder, Cnaphalocrocis medinalis (Guenee) (Lepidoptera: Pyralidae) is assuming the major pest status in view of its severe damage. There was severe outbreak of rice leaf folder in three different states of India during different periods between 2010 and 2022. Extensive field surveys were conducted from the locations during this outbreak period and the ground-truth data was collected. A total of 272 rice plants with varying levels of leaf folder damage symptoms were sampled across 3 major rice growing regions of India to collect the spectral reflectance data using FieldSpec-3 Hi-Res hyperspectral spectroradiometer (spectral range: 350–2500 nm, ASD Inc., USA) during 3 seasons. The spectral data was interpolated using ASD ViewSpecPro software in the post-processing to produce values at each nanometer. Multinomial logistic regression analysis (MLR) was performed to identify the sensitive bands (396, 675, 764, 1123 and 1458 nm) specific to leaf folder damage. Principal component analysis (PCA) was performed to identify the optimum combination of these five sensitive bands. The new hyperspectral indices identified in this study specific to leaf folder damage were found to perform better than the relevant spectral indices published earlier. The identified principal components (PCs) were used to build MLR models for assessing the rice leaf folder severity. Model outputs were validated using independent data sets. The classification accuracy of the model using the first four PCs as independent variables was in the range of 33 to 72. This study suggests a new set of hyperspectral indices specific to leaf folder damage to assess the area-wide pest damage in rice. |
| format | Article |
| id | doaj-art-3cbf4dc725594da89447dca634615bb3 |
| institution | DOAJ |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-3cbf4dc725594da89447dca634615bb32025-08-20T02:52:23ZengElsevierSmart Agricultural Technology2772-37552025-03-011010075710.1016/j.atech.2024.100757Remote sensing of rice leaf folder damage using ground-based hyperspectral radiometryMathyam Prabhakar0Ch Padmavathi1Merugu Thirupathi2Srasvan Kumar Golla3Uppu Sai Sravan4G. Ramachandra Rao5Madduri Kalpana6Vallabuni Sailaja7Pebbeti Chandana8Yenumula G. Prasad9M. Srinivasa Rao10V.K. Singh11Rajbir Singh12ICAR-Central Research Institute for Dryland Agriculture, Hyderabad, Telangana 500059, India; Corresponding author.ICAR-Indian Institute of Rice Research, Hyderabad, Telangana 500030, IndiaICAR-Central Research Institute for Dryland Agriculture, Hyderabad, Telangana 500059, IndiaICAR-Central Research Institute for Dryland Agriculture, Hyderabad, Telangana 500059, IndiaICAR-Central Research Institute for Dryland Agriculture, Hyderabad, Telangana 500059, IndiaICAR-Central Research Institute for Dryland Agriculture, Hyderabad, Telangana 500059, IndiaICAR-Central Research Institute for Dryland Agriculture, Hyderabad, Telangana 500059, IndiaForest College and Research Institute, Mulugu, Telangana 502279, IndiaICAR-Central Research Institute for Dryland Agriculture, Hyderabad, Telangana 500059, IndiaICAR-Central Institute for Cotton Research, Nagpur, Maharashtra 441108, IndiaICAR-Central Research Institute for Dryland Agriculture, Hyderabad, Telangana 500059, IndiaICAR-Central Research Institute for Dryland Agriculture, Hyderabad, Telangana 500059, IndiaNatural Resource Management Division, ICAR, New Delhi 110001, IndiaRice is the staple food for more than 60 % of the world's population and pest damage is one of the major limiting factors of rice production in India. Rice leaf folder, Cnaphalocrocis medinalis (Guenee) (Lepidoptera: Pyralidae) is assuming the major pest status in view of its severe damage. There was severe outbreak of rice leaf folder in three different states of India during different periods between 2010 and 2022. Extensive field surveys were conducted from the locations during this outbreak period and the ground-truth data was collected. A total of 272 rice plants with varying levels of leaf folder damage symptoms were sampled across 3 major rice growing regions of India to collect the spectral reflectance data using FieldSpec-3 Hi-Res hyperspectral spectroradiometer (spectral range: 350–2500 nm, ASD Inc., USA) during 3 seasons. The spectral data was interpolated using ASD ViewSpecPro software in the post-processing to produce values at each nanometer. Multinomial logistic regression analysis (MLR) was performed to identify the sensitive bands (396, 675, 764, 1123 and 1458 nm) specific to leaf folder damage. Principal component analysis (PCA) was performed to identify the optimum combination of these five sensitive bands. The new hyperspectral indices identified in this study specific to leaf folder damage were found to perform better than the relevant spectral indices published earlier. The identified principal components (PCs) were used to build MLR models for assessing the rice leaf folder severity. Model outputs were validated using independent data sets. The classification accuracy of the model using the first four PCs as independent variables was in the range of 33 to 72. This study suggests a new set of hyperspectral indices specific to leaf folder damage to assess the area-wide pest damage in rice.http://www.sciencedirect.com/science/article/pii/S2772375524003617Rice leaf folderHyperspectral remote sensingSensitive bandsPrincipal component analysisMultinomial logistic regression |
| spellingShingle | Mathyam Prabhakar Ch Padmavathi Merugu Thirupathi Srasvan Kumar Golla Uppu Sai Sravan G. Ramachandra Rao Madduri Kalpana Vallabuni Sailaja Pebbeti Chandana Yenumula G. Prasad M. Srinivasa Rao V.K. Singh Rajbir Singh Remote sensing of rice leaf folder damage using ground-based hyperspectral radiometry Smart Agricultural Technology Rice leaf folder Hyperspectral remote sensing Sensitive bands Principal component analysis Multinomial logistic regression |
| title | Remote sensing of rice leaf folder damage using ground-based hyperspectral radiometry |
| title_full | Remote sensing of rice leaf folder damage using ground-based hyperspectral radiometry |
| title_fullStr | Remote sensing of rice leaf folder damage using ground-based hyperspectral radiometry |
| title_full_unstemmed | Remote sensing of rice leaf folder damage using ground-based hyperspectral radiometry |
| title_short | Remote sensing of rice leaf folder damage using ground-based hyperspectral radiometry |
| title_sort | remote sensing of rice leaf folder damage using ground based hyperspectral radiometry |
| topic | Rice leaf folder Hyperspectral remote sensing Sensitive bands Principal component analysis Multinomial logistic regression |
| url | http://www.sciencedirect.com/science/article/pii/S2772375524003617 |
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