Crop waterlogging image classification detection and implementation method based on Hadoop
A crop waterlogging image classification detection and implementation method based on Hadoop is characterized by comprising the following steps: a) collecting crop field waterlogging images, and performing image correction and preprocessing and image principal component analysis; b) uploading the im...
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 | GE DAOKUO CAO HONGXIN ZHANG WEIXIN ZHANG WENYU XUAN HUI YU LINHUI XIA JI'AN |
description | A crop waterlogging image classification detection and implementation method based on Hadoop is characterized by comprising the following steps: a) collecting crop field waterlogging images, and performing image correction and preprocessing and image principal component analysis; b) uploading the image matrix to a Hadoop computing platform for distributed storage, and compiling a parallel neural network algorithm; and c) carrying out algorithm modeling and prediction, and carrying out classification analysis on crop waterlogging image information. Distributed parallel classification analysis of the image data under crop disaster stress is performed through the Hadoop framework so that modeling and prediction speed of the classification algorithm can be accelerated, and the larger the imagedata size is, the more obvious the advantage is compared with the single-machine mode. A neural network algorithm is compiled by using a Scala language, and the algorithm is suitable for parallel operation under a Hadoop fra |
format | Patent |
fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN112070062A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN112070062A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN112070062A3</originalsourceid><addsrcrecordid>eNqNirEKwjAURbM4iPoPzw8Q0go6S1A6OTm5lGdyGwNpXmgC_r5F_QCncw_3LNXdTJLpxRVTFO9D8hRG9iAbuZQwBMs1SCKHCvtZnNyc5IgRqX7PEfUpjh5c4Gj2jp1IXqvFwLFg8-NKbS_nm-l2yNKjZLZIqL25Nk2rj1of2tP-n-YNb4Q7ZA</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Crop waterlogging image classification detection and implementation method based on Hadoop</title><source>esp@cenet</source><creator>GE DAOKUO ; CAO HONGXIN ; ZHANG WEIXIN ; ZHANG WENYU ; XUAN HUI ; YU LINHUI ; XIA JI'AN</creator><creatorcontrib>GE DAOKUO ; CAO HONGXIN ; ZHANG WEIXIN ; ZHANG WENYU ; XUAN HUI ; YU LINHUI ; XIA JI'AN</creatorcontrib><description>A crop waterlogging image classification detection and implementation method based on Hadoop is characterized by comprising the following steps: a) collecting crop field waterlogging images, and performing image correction and preprocessing and image principal component analysis; b) uploading the image matrix to a Hadoop computing platform for distributed storage, and compiling a parallel neural network algorithm; and c) carrying out algorithm modeling and prediction, and carrying out classification analysis on crop waterlogging image information. Distributed parallel classification analysis of the image data under crop disaster stress is performed through the Hadoop framework so that modeling and prediction speed of the classification algorithm can be accelerated, and the larger the imagedata size is, the more obvious the advantage is compared with the single-machine mode. A neural network algorithm is compiled by using a Scala language, and the algorithm is suitable for parallel operation under a Hadoop fra</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; HANDLING RECORD CARRIERS ; PHYSICS ; PRESENTATION OF DATA ; RECOGNITION OF DATA ; RECORD CARRIERS</subject><creationdate>2020</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=20201211&DB=EPODOC&CC=CN&NR=112070062A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76419</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20201211&DB=EPODOC&CC=CN&NR=112070062A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>GE DAOKUO</creatorcontrib><creatorcontrib>CAO HONGXIN</creatorcontrib><creatorcontrib>ZHANG WEIXIN</creatorcontrib><creatorcontrib>ZHANG WENYU</creatorcontrib><creatorcontrib>XUAN HUI</creatorcontrib><creatorcontrib>YU LINHUI</creatorcontrib><creatorcontrib>XIA JI'AN</creatorcontrib><title>Crop waterlogging image classification detection and implementation method based on Hadoop</title><description>A crop waterlogging image classification detection and implementation method based on Hadoop is characterized by comprising the following steps: a) collecting crop field waterlogging images, and performing image correction and preprocessing and image principal component analysis; b) uploading the image matrix to a Hadoop computing platform for distributed storage, and compiling a parallel neural network algorithm; and c) carrying out algorithm modeling and prediction, and carrying out classification analysis on crop waterlogging image information. Distributed parallel classification analysis of the image data under crop disaster stress is performed through the Hadoop framework so that modeling and prediction speed of the classification algorithm can be accelerated, and the larger the imagedata size is, the more obvious the advantage is compared with the single-machine mode. A neural network algorithm is compiled by using a Scala language, and the algorithm is suitable for parallel operation under a Hadoop fra</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>HANDLING RECORD CARRIERS</subject><subject>PHYSICS</subject><subject>PRESENTATION OF DATA</subject><subject>RECOGNITION OF DATA</subject><subject>RECORD CARRIERS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2020</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNirEKwjAURbM4iPoPzw8Q0go6S1A6OTm5lGdyGwNpXmgC_r5F_QCncw_3LNXdTJLpxRVTFO9D8hRG9iAbuZQwBMs1SCKHCvtZnNyc5IgRqX7PEfUpjh5c4Gj2jp1IXqvFwLFg8-NKbS_nm-l2yNKjZLZIqL25Nk2rj1of2tP-n-YNb4Q7ZA</recordid><startdate>20201211</startdate><enddate>20201211</enddate><creator>GE DAOKUO</creator><creator>CAO HONGXIN</creator><creator>ZHANG WEIXIN</creator><creator>ZHANG WENYU</creator><creator>XUAN HUI</creator><creator>YU LINHUI</creator><creator>XIA JI'AN</creator><scope>EVB</scope></search><sort><creationdate>20201211</creationdate><title>Crop waterlogging image classification detection and implementation method based on Hadoop</title><author>GE DAOKUO ; CAO HONGXIN ; ZHANG WEIXIN ; ZHANG WENYU ; XUAN HUI ; YU LINHUI ; XIA JI'AN</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN112070062A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2020</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>HANDLING RECORD CARRIERS</topic><topic>PHYSICS</topic><topic>PRESENTATION OF DATA</topic><topic>RECOGNITION OF DATA</topic><topic>RECORD CARRIERS</topic><toplevel>online_resources</toplevel><creatorcontrib>GE DAOKUO</creatorcontrib><creatorcontrib>CAO HONGXIN</creatorcontrib><creatorcontrib>ZHANG WEIXIN</creatorcontrib><creatorcontrib>ZHANG WENYU</creatorcontrib><creatorcontrib>XUAN HUI</creatorcontrib><creatorcontrib>YU LINHUI</creatorcontrib><creatorcontrib>XIA JI'AN</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>GE DAOKUO</au><au>CAO HONGXIN</au><au>ZHANG WEIXIN</au><au>ZHANG WENYU</au><au>XUAN HUI</au><au>YU LINHUI</au><au>XIA JI'AN</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Crop waterlogging image classification detection and implementation method based on Hadoop</title><date>2020-12-11</date><risdate>2020</risdate><abstract>A crop waterlogging image classification detection and implementation method based on Hadoop is characterized by comprising the following steps: a) collecting crop field waterlogging images, and performing image correction and preprocessing and image principal component analysis; b) uploading the image matrix to a Hadoop computing platform for distributed storage, and compiling a parallel neural network algorithm; and c) carrying out algorithm modeling and prediction, and carrying out classification analysis on crop waterlogging image information. Distributed parallel classification analysis of the image data under crop disaster stress is performed through the Hadoop framework so that modeling and prediction speed of the classification algorithm can be accelerated, and the larger the imagedata size is, the more obvious the advantage is compared with the single-machine mode. A neural network algorithm is compiled by using a Scala language, and the algorithm is suitable for parallel operation under a Hadoop fra</abstract><oa>free_for_read</oa></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | |
ispartof | |
issn | |
language | chi ; eng |
recordid | cdi_epo_espacenet_CN112070062A |
source | esp@cenet |
subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING HANDLING RECORD CARRIERS PHYSICS PRESENTATION OF DATA RECOGNITION OF DATA RECORD CARRIERS |
title | Crop waterlogging image classification detection and implementation method based on Hadoop |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-08T01%3A33%3A58IST&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=GE%20DAOKUO&rft.date=2020-12-11&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN112070062A%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 |