Development of an early warning system for brown planthopper (BPH) (Nilaparvata lugens) in rice farming using multispectral remote sensing
The spread of rice pests such as BPH in tropical areas is one of the best-known yield lost factors. Remote sensing can support precision farming practices for determining the location of spreads and using pesticide in the right place. In a specifically conducive environment like high temperature and...
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| Veröffentlicht in: | Precision agriculture 2016-08, Vol.17 (4), p.377-391 |
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| description | The spread of rice pests such as BPH in tropical areas is one of the best-known yield lost factors. Remote sensing can support precision farming practices for determining the location of spreads and using pesticide in the right place. In a specifically conducive environment like high temperature and heavy rainfall, BPH population will increase. To address this issue, detection of sheath blight in rice farming was examined by using SPOT-5 images. Also, the extraction of weather data derived from Landsat images for comparing with the BPH infestation was undertaken. Results showed that all the indices that recognize infected plants are significant at α = 0.01. Examination of the association between the disease indices indicated that band 3 (near infrared) and band 4 (mid infrared) in SPOT-5 images have a relatively high correlation for detecting diseased part from healthy ones. The selected indices declared better association for detecting healthy plants from diseased ones. Image investigations revealed that BPH were existing at the higher limits of tolerable temperatures when in the form of nymphs. With the knowledge that the late growth stage of plants has more severe BPH attacks, the results stated that BPH outbreak is particularly obvious in the north-west corner and middle regions of the maps and it is more likely to happen in specified ranges of temperature and RH, i.e. 29 °C |
| doi_str_mv | 10.1007/s11119-015-9422-9 |
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Remote sensing can support precision farming practices for determining the location of spreads and using pesticide in the right place. In a specifically conducive environment like high temperature and heavy rainfall, BPH population will increase. To address this issue, detection of sheath blight in rice farming was examined by using SPOT-5 images. Also, the extraction of weather data derived from Landsat images for comparing with the BPH infestation was undertaken. Results showed that all the indices that recognize infected plants are significant at α = 0.01. Examination of the association between the disease indices indicated that band 3 (near infrared) and band 4 (mid infrared) in SPOT-5 images have a relatively high correlation for detecting diseased part from healthy ones. The selected indices declared better association for detecting healthy plants from diseased ones. Image investigations revealed that BPH were existing at the higher limits of tolerable temperatures when in the form of nymphs. With the knowledge that the late growth stage of plants has more severe BPH attacks, the results stated that BPH outbreak is particularly obvious in the north-west corner and middle regions of the maps and it is more likely to happen in specified ranges of temperature and RH, i.e. 29 °C </description><identifier>ISSN: 1385-2256</identifier><identifier>EISSN: 1573-1618</identifier><identifier>DOI: 10.1007/s11119-015-9422-9</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Agricultural practices ; Agricultural production ; Agriculture ; Analysis ; Atmospheric Sciences ; Biomedical and Life Sciences ; blight ; Chemistry and Earth Sciences ; Chlorophyll ; Civil engineering ; Computer Science ; early development ; early warning systems ; Farming ; Flowers & plants ; High temperature ; Humidity ; Information science ; Irrigation ; Landsat ; Life Sciences ; meteorological data ; Nilaparvata lugens ; nymphs ; Pest control ; Pesticides ; pests ; Physics ; Plant diseases ; Population ; population growth ; precision agriculture ; Precision farming ; Rain ; Regression analysis ; Remote sensing ; Remote Sensing/Photogrammetry ; Rice ; Soil Science & Conservation ; Statistics for Engineering ; Studies ; Temperature ; Vegetation</subject><ispartof>Precision agriculture, 2016-08, Vol.17 (4), p.377-391</ispartof><rights>Springer Science+Business Media New York 2015</rights><rights>Springer Science+Business Media New York 2016</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c416t-f8a3f25d877d6da2621cf9d57c9a4579ef0f3ee5b00e08cc61d5320fee74c42b3</citedby><cites>FETCH-LOGICAL-c416t-f8a3f25d877d6da2621cf9d57c9a4579ef0f3ee5b00e08cc61d5320fee74c42b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11119-015-9422-9$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11119-015-9422-9$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Ghobadifar, Faranak</creatorcontrib><creatorcontrib>Wayayok Aimrun</creatorcontrib><creatorcontrib>Mustafa Neamah Jebur</creatorcontrib><title>Development of an early warning system for brown planthopper (BPH) (Nilaparvata lugens) in rice farming using multispectral remote sensing</title><title>Precision agriculture</title><addtitle>Precision Agric</addtitle><description>The spread of rice pests such as BPH in tropical areas is one of the best-known yield lost factors. Remote sensing can support precision farming practices for determining the location of spreads and using pesticide in the right place. In a specifically conducive environment like high temperature and heavy rainfall, BPH population will increase. To address this issue, detection of sheath blight in rice farming was examined by using SPOT-5 images. Also, the extraction of weather data derived from Landsat images for comparing with the BPH infestation was undertaken. Results showed that all the indices that recognize infected plants are significant at α = 0.01. Examination of the association between the disease indices indicated that band 3 (near infrared) and band 4 (mid infrared) in SPOT-5 images have a relatively high correlation for detecting diseased part from healthy ones. The selected indices declared better association for detecting healthy plants from diseased ones. Image investigations revealed that BPH were existing at the higher limits of tolerable temperatures when in the form of nymphs. With the knowledge that the late growth stage of plants has more severe BPH attacks, the results stated that BPH outbreak is particularly obvious in the north-west corner and middle regions of the maps and it is more likely to happen in specified ranges of temperature and RH, i.e. 29 °C </description><subject>Agricultural practices</subject><subject>Agricultural production</subject><subject>Agriculture</subject><subject>Analysis</subject><subject>Atmospheric Sciences</subject><subject>Biomedical and Life Sciences</subject><subject>blight</subject><subject>Chemistry and Earth Sciences</subject><subject>Chlorophyll</subject><subject>Civil engineering</subject><subject>Computer Science</subject><subject>early development</subject><subject>early warning systems</subject><subject>Farming</subject><subject>Flowers & plants</subject><subject>High temperature</subject><subject>Humidity</subject><subject>Information 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Agric</stitle><date>2016-08-01</date><risdate>2016</risdate><volume>17</volume><issue>4</issue><spage>377</spage><epage>391</epage><pages>377-391</pages><issn>1385-2256</issn><eissn>1573-1618</eissn><abstract>The spread of rice pests such as BPH in tropical areas is one of the best-known yield lost factors. Remote sensing can support precision farming practices for determining the location of spreads and using pesticide in the right place. In a specifically conducive environment like high temperature and heavy rainfall, BPH population will increase. To address this issue, detection of sheath blight in rice farming was examined by using SPOT-5 images. Also, the extraction of weather data derived from Landsat images for comparing with the BPH infestation was undertaken. Results showed that all the indices that recognize infected plants are significant at α = 0.01. Examination of the association between the disease indices indicated that band 3 (near infrared) and band 4 (mid infrared) in SPOT-5 images have a relatively high correlation for detecting diseased part from healthy ones. The selected indices declared better association for detecting healthy plants from diseased ones. Image investigations revealed that BPH were existing at the higher limits of tolerable temperatures when in the form of nymphs. With the knowledge that the late growth stage of plants has more severe BPH attacks, the results stated that BPH outbreak is particularly obvious in the north-west corner and middle regions of the maps and it is more likely to happen in specified ranges of temperature and RH, i.e. 29 °C </abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11119-015-9422-9</doi><tpages>15</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Agricultural practices Agricultural production Agriculture Analysis Atmospheric Sciences Biomedical and Life Sciences blight Chemistry and Earth Sciences Chlorophyll Civil engineering Computer Science early development early warning systems Farming Flowers & plants High temperature Humidity Information science Irrigation Landsat Life Sciences meteorological data Nilaparvata lugens nymphs Pest control Pesticides pests Physics Plant diseases Population population growth precision agriculture Precision farming Rain Regression analysis Remote sensing Remote Sensing/Photogrammetry Rice Soil Science & Conservation Statistics for Engineering Studies Temperature Vegetation |
| title | Development of an early warning system for brown planthopper (BPH) (Nilaparvata lugens) in rice farming using multispectral remote sensing |
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