Crop disease and pest identification method and system based on hyperspectral remote sensing image
The invention provides a crop disease and pest identification method and system based on a hyperspectral remote sensing image, and the method comprises the steps: extracting images of a near-infrared band, a red band and a neighborhood band in the hyperspectral remote sensing image to obtain a first...
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Patent |
| Sprache: | chi ; eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | |
| container_volume | |
| creator | LIU YATONG LIU MENG ZHU XIAOJUN DUAN SHAOPENG CHEN YUEYANG |
| description | The invention provides a crop disease and pest identification method and system based on a hyperspectral remote sensing image, and the method comprises the steps: extracting images of a near-infrared band, a red band and a neighborhood band in the hyperspectral remote sensing image to obtain a first image and a second image, and obtaining a third image according to the first image and the second image, segmenting the third image to obtain at least one planting area of the crop; obtaining a spectrum characteristic curve of a crop growth stage, and reducing the number of spectrum channel images of the hyperspectral remote sensing image according to the curve so as to obtain a dimension-reduced spectrum remote sensing image of the planting area; and inputting the dimension-reduced spectral remote sensing image of the planting area into the trained deep learning model to obtain a planting area pest and disease identification result. The number of channels in the hyperspectral remote sensing image is effectively r |
| format | Patent |
| fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN116912678A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN116912678A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN116912678A3</originalsourceid><addsrcrecordid>eNqNyjsOglAQRmEaC6PuYVyABZqgloZorKzsycD9gUm4j9yZht1LjAuwOsV31kVb55jIiYIVxMFRghqJQzDppWOTGMjDxui-rLMaPLXL7mihcU7ImtBZ5okyfDSQIqiEgcTzgG2x6nlS7H7dFPvH_V0_D0ixgSbuEGBN_SrL6loeq_Pldvrn-QBrTz5n</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Crop disease and pest identification method and system based on hyperspectral remote sensing image</title><source>esp@cenet</source><creator>LIU YATONG ; LIU MENG ; ZHU XIAOJUN ; DUAN SHAOPENG ; CHEN YUEYANG</creator><creatorcontrib>LIU YATONG ; LIU MENG ; ZHU XIAOJUN ; DUAN SHAOPENG ; CHEN YUEYANG</creatorcontrib><description>The invention provides a crop disease and pest identification method and system based on a hyperspectral remote sensing image, and the method comprises the steps: extracting images of a near-infrared band, a red band and a neighborhood band in the hyperspectral remote sensing image to obtain a first image and a second image, and obtaining a third image according to the first image and the second image, segmenting the third image to obtain at least one planting area of the crop; obtaining a spectrum characteristic curve of a crop growth stage, and reducing the number of spectrum channel images of the hyperspectral remote sensing image according to the curve so as to obtain a dimension-reduced spectrum remote sensing image of the planting area; and inputting the dimension-reduced spectral remote sensing image of the planting area into the trained deep learning model to obtain a planting area pest and disease identification result. The number of channels in the hyperspectral remote sensing image is effectively r</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; PHYSICS</subject><creationdate>2023</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20231020&DB=EPODOC&CC=CN&NR=116912678A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20231020&DB=EPODOC&CC=CN&NR=116912678A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>LIU YATONG</creatorcontrib><creatorcontrib>LIU MENG</creatorcontrib><creatorcontrib>ZHU XIAOJUN</creatorcontrib><creatorcontrib>DUAN SHAOPENG</creatorcontrib><creatorcontrib>CHEN YUEYANG</creatorcontrib><title>Crop disease and pest identification method and system based on hyperspectral remote sensing image</title><description>The invention provides a crop disease and pest identification method and system based on a hyperspectral remote sensing image, and the method comprises the steps: extracting images of a near-infrared band, a red band and a neighborhood band in the hyperspectral remote sensing image to obtain a first image and a second image, and obtaining a third image according to the first image and the second image, segmenting the third image to obtain at least one planting area of the crop; obtaining a spectrum characteristic curve of a crop growth stage, and reducing the number of spectrum channel images of the hyperspectral remote sensing image according to the curve so as to obtain a dimension-reduced spectrum remote sensing image of the planting area; and inputting the dimension-reduced spectral remote sensing image of the planting area into the trained deep learning model to obtain a planting area pest and disease identification result. The number of channels in the hyperspectral remote sensing image is effectively r</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNyjsOglAQRmEaC6PuYVyABZqgloZorKzsycD9gUm4j9yZht1LjAuwOsV31kVb55jIiYIVxMFRghqJQzDppWOTGMjDxui-rLMaPLXL7mihcU7ImtBZ5okyfDSQIqiEgcTzgG2x6nlS7H7dFPvH_V0_D0ixgSbuEGBN_SrL6loeq_Pldvrn-QBrTz5n</recordid><startdate>20231020</startdate><enddate>20231020</enddate><creator>LIU YATONG</creator><creator>LIU MENG</creator><creator>ZHU XIAOJUN</creator><creator>DUAN SHAOPENG</creator><creator>CHEN YUEYANG</creator><scope>EVB</scope></search><sort><creationdate>20231020</creationdate><title>Crop disease and pest identification method and system based on hyperspectral remote sensing image</title><author>LIU YATONG ; LIU MENG ; ZHU XIAOJUN ; DUAN SHAOPENG ; CHEN YUEYANG</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN116912678A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2023</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>LIU YATONG</creatorcontrib><creatorcontrib>LIU MENG</creatorcontrib><creatorcontrib>ZHU XIAOJUN</creatorcontrib><creatorcontrib>DUAN SHAOPENG</creatorcontrib><creatorcontrib>CHEN YUEYANG</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>LIU YATONG</au><au>LIU MENG</au><au>ZHU XIAOJUN</au><au>DUAN SHAOPENG</au><au>CHEN YUEYANG</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Crop disease and pest identification method and system based on hyperspectral remote sensing image</title><date>2023-10-20</date><risdate>2023</risdate><abstract>The invention provides a crop disease and pest identification method and system based on a hyperspectral remote sensing image, and the method comprises the steps: extracting images of a near-infrared band, a red band and a neighborhood band in the hyperspectral remote sensing image to obtain a first image and a second image, and obtaining a third image according to the first image and the second image, segmenting the third image to obtain at least one planting area of the crop; obtaining a spectrum characteristic curve of a crop growth stage, and reducing the number of spectrum channel images of the hyperspectral remote sensing image according to the curve so as to obtain a dimension-reduced spectrum remote sensing image of the planting area; and inputting the dimension-reduced spectral remote sensing image of the planting area into the trained deep learning model to obtain a planting area pest and disease identification result. The number of channels in the hyperspectral remote sensing image is effectively r</abstract><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | |
| ispartof | |
| issn | |
| language | chi ; eng |
| recordid | cdi_epo_espacenet_CN116912678A |
| source | esp@cenet |
| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING PHYSICS |
| title | Crop disease and pest identification method and system based on hyperspectral remote sensing image |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-01T09%3A44%3A30IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=LIU%20YATONG&rft.date=2023-10-20&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN116912678A%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |