A Review of the Application of Hyperspectral Imaging Technology in Agricultural Crop Economics
China is a large agricultural country, and the crop economy holds an important place in the national economy. The identification of crop diseases and pests, as well as the non-destructive classification of crops, has always been a challenge in agricultural development, hindering the rapid growth of...
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description | China is a large agricultural country, and the crop economy holds an important place in the national economy. The identification of crop diseases and pests, as well as the non-destructive classification of crops, has always been a challenge in agricultural development, hindering the rapid growth of the agricultural economy. Hyperspectral imaging technology combines imaging and spectral techniques, using hyperspectral cameras to acquire raw image data of crops. After correcting and preprocessing the raw image data to obtain the required spectral features, it becomes possible to achieve the rapid non-destructive detection of crop diseases and pests, as well as the non-destructive classification and identification of agricultural products. This paper first provides an overview of the current applications of hyperspectral imaging technology in crops both domestically and internationally. It then summarizes the methods of hyperspectral data acquisition and application scenarios. Subsequently, it organizes the processing of hyperspectral data for crop disease and pest detection and classification, deriving relevant preprocessing and analysis methods for hyperspectral data. Finally, it conducts a detailed analysis of classic cases using hyperspectral imaging technology for detecting crop diseases and pests and non-destructive classification, while also analyzing and summarizing the future development trends of hyperspectral imaging technology in agricultural production. The non-destructive rapid detection and classification technology of hyperspectral imaging can effectively select qualified crops and classify crops of different qualities, ensuring the quality of agricultural products. In conclusion, hyperspectral imaging technology can effectively serve the agricultural economy, making agricultural production more intelligent and holding significant importance for the development of agriculture in China. |
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The identification of crop diseases and pests, as well as the non-destructive classification of crops, has always been a challenge in agricultural development, hindering the rapid growth of the agricultural economy. Hyperspectral imaging technology combines imaging and spectral techniques, using hyperspectral cameras to acquire raw image data of crops. After correcting and preprocessing the raw image data to obtain the required spectral features, it becomes possible to achieve the rapid non-destructive detection of crop diseases and pests, as well as the non-destructive classification and identification of agricultural products. This paper first provides an overview of the current applications of hyperspectral imaging technology in crops both domestically and internationally. It then summarizes the methods of hyperspectral data acquisition and application scenarios. Subsequently, it organizes the processing of hyperspectral data for crop disease and pest detection and classification, deriving relevant preprocessing and analysis methods for hyperspectral data. Finally, it conducts a detailed analysis of classic cases using hyperspectral imaging technology for detecting crop diseases and pests and non-destructive classification, while also analyzing and summarizing the future development trends of hyperspectral imaging technology in agricultural production. The non-destructive rapid detection and classification technology of hyperspectral imaging can effectively select qualified crops and classify crops of different qualities, ensuring the quality of agricultural products. In conclusion, hyperspectral imaging technology can effectively serve the agricultural economy, making agricultural production more intelligent and holding significant importance for the development of agriculture in China.</description><identifier>ISSN: 2079-6412</identifier><identifier>EISSN: 2079-6412</identifier><identifier>DOI: 10.3390/coatings14101285</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Accuracy ; Agricultural industry ; Agricultural production ; Agriculture ; Algorithms ; Artificial intelligence ; Classification ; Crop damage ; Crop diseases ; Crop production ; Cultural heritage ; Data acquisition ; Data analysis ; Data entry ; Deep learning ; Disease prevention ; Economics ; Hyperspectral imaging ; Identification ; Image acquisition ; Imaging systems ; Leaves ; Machine learning ; Medical imaging ; Methods ; Mold ; Nondestructive testing ; Pests ; Plant diseases ; Preprocessing ; Spectral methods ; Technology assessment ; Unmanned aerial vehicles</subject><ispartof>Coatings (Basel), 2024-10, Vol.14 (10), p.1285</ispartof><rights>COPYRIGHT 2024 MDPI AG</rights><rights>2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c235t-f1d1aab2c36f09710cf32042ce13b321aeba1ae6131453c8ff4544d0fa8c9ddd3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Wu, Jinxing</creatorcontrib><creatorcontrib>Zhang, Yi</creatorcontrib><creatorcontrib>Hu, Pengfei</creatorcontrib><creatorcontrib>Wu, Yanying</creatorcontrib><title>A Review of the Application of Hyperspectral Imaging Technology in Agricultural Crop Economics</title><title>Coatings (Basel)</title><description>China is a large agricultural country, and the crop economy holds an important place in the national economy. The identification of crop diseases and pests, as well as the non-destructive classification of crops, has always been a challenge in agricultural development, hindering the rapid growth of the agricultural economy. Hyperspectral imaging technology combines imaging and spectral techniques, using hyperspectral cameras to acquire raw image data of crops. After correcting and preprocessing the raw image data to obtain the required spectral features, it becomes possible to achieve the rapid non-destructive detection of crop diseases and pests, as well as the non-destructive classification and identification of agricultural products. This paper first provides an overview of the current applications of hyperspectral imaging technology in crops both domestically and internationally. It then summarizes the methods of hyperspectral data acquisition and application scenarios. Subsequently, it organizes the processing of hyperspectral data for crop disease and pest detection and classification, deriving relevant preprocessing and analysis methods for hyperspectral data. Finally, it conducts a detailed analysis of classic cases using hyperspectral imaging technology for detecting crop diseases and pests and non-destructive classification, while also analyzing and summarizing the future development trends of hyperspectral imaging technology in agricultural production. The non-destructive rapid detection and classification technology of hyperspectral imaging can effectively select qualified crops and classify crops of different qualities, ensuring the quality of agricultural products. In conclusion, hyperspectral imaging technology can effectively serve the agricultural economy, making agricultural production more intelligent and holding significant importance for the development of agriculture in China.</description><subject>Accuracy</subject><subject>Agricultural industry</subject><subject>Agricultural production</subject><subject>Agriculture</subject><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Classification</subject><subject>Crop damage</subject><subject>Crop diseases</subject><subject>Crop production</subject><subject>Cultural heritage</subject><subject>Data acquisition</subject><subject>Data analysis</subject><subject>Data entry</subject><subject>Deep learning</subject><subject>Disease prevention</subject><subject>Economics</subject><subject>Hyperspectral imaging</subject><subject>Identification</subject><subject>Image acquisition</subject><subject>Imaging systems</subject><subject>Leaves</subject><subject>Machine learning</subject><subject>Medical imaging</subject><subject>Methods</subject><subject>Mold</subject><subject>Nondestructive testing</subject><subject>Pests</subject><subject>Plant diseases</subject><subject>Preprocessing</subject><subject>Spectral methods</subject><subject>Technology assessment</subject><subject>Unmanned aerial vehicles</subject><issn>2079-6412</issn><issn>2079-6412</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpdUN9LwzAQDqLg0L37GPC5M5ekbfpYxnSDgSDz1ZKlSZfRJTXplP33ZswH8Q7ujuP7vvuB0AOQGWMVeVJejtZ1ETgQoCK_QhNKyiorONDrP_Utmsa4J8kqYAKqCfqo8Zv-svobe4PHncb1MPRWJTnvzq3ladAhDlqNQfZ4dZBdmoM3Wu2c7313wtbhugtWHfvxeIbMgx_wQnnnD1bFe3RjZB_19DffoffnxWa-zNavL6t5vc4UZfmYGWhByi1VrDCkKoEowyjhVGlgW0ZB6q1MoQAGPGdKGMNzzltipFBV27bsDj1edIfgP486js3eH4NLIxsGlBQEhKAJNbugOtnrxjrj01UqeavTst5pY1O_FsCZKHNWJgK5EFTwMQZtmiHYgwynBkhz_nzz__PsB15KeKE</recordid><startdate>20241001</startdate><enddate>20241001</enddate><creator>Wu, Jinxing</creator><creator>Zhang, Yi</creator><creator>Hu, Pengfei</creator><creator>Wu, Yanying</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>JG9</scope><scope>KB.</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope></search><sort><creationdate>20241001</creationdate><title>A Review of the Application of Hyperspectral Imaging Technology in Agricultural Crop Economics</title><author>Wu, Jinxing ; Zhang, Yi ; Hu, Pengfei ; Wu, Yanying</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c235t-f1d1aab2c36f09710cf32042ce13b321aeba1ae6131453c8ff4544d0fa8c9ddd3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Accuracy</topic><topic>Agricultural industry</topic><topic>Agricultural production</topic><topic>Agriculture</topic><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>Classification</topic><topic>Crop damage</topic><topic>Crop diseases</topic><topic>Crop production</topic><topic>Cultural heritage</topic><topic>Data acquisition</topic><topic>Data analysis</topic><topic>Data entry</topic><topic>Deep learning</topic><topic>Disease prevention</topic><topic>Economics</topic><topic>Hyperspectral imaging</topic><topic>Identification</topic><topic>Image acquisition</topic><topic>Imaging systems</topic><topic>Leaves</topic><topic>Machine learning</topic><topic>Medical imaging</topic><topic>Methods</topic><topic>Mold</topic><topic>Nondestructive testing</topic><topic>Pests</topic><topic>Plant diseases</topic><topic>Preprocessing</topic><topic>Spectral methods</topic><topic>Technology assessment</topic><topic>Unmanned aerial vehicles</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wu, Jinxing</creatorcontrib><creatorcontrib>Zhang, Yi</creatorcontrib><creatorcontrib>Hu, Pengfei</creatorcontrib><creatorcontrib>Wu, Yanying</creatorcontrib><collection>CrossRef</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>Materials Research Database</collection><collection>Materials Science Database</collection><collection>Materials Science Collection</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>Coatings (Basel)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wu, Jinxing</au><au>Zhang, Yi</au><au>Hu, Pengfei</au><au>Wu, Yanying</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Review of the Application of Hyperspectral Imaging Technology in Agricultural Crop Economics</atitle><jtitle>Coatings (Basel)</jtitle><date>2024-10-01</date><risdate>2024</risdate><volume>14</volume><issue>10</issue><spage>1285</spage><pages>1285-</pages><issn>2079-6412</issn><eissn>2079-6412</eissn><abstract>China is a large agricultural country, and the crop economy holds an important place in the national economy. The identification of crop diseases and pests, as well as the non-destructive classification of crops, has always been a challenge in agricultural development, hindering the rapid growth of the agricultural economy. Hyperspectral imaging technology combines imaging and spectral techniques, using hyperspectral cameras to acquire raw image data of crops. After correcting and preprocessing the raw image data to obtain the required spectral features, it becomes possible to achieve the rapid non-destructive detection of crop diseases and pests, as well as the non-destructive classification and identification of agricultural products. This paper first provides an overview of the current applications of hyperspectral imaging technology in crops both domestically and internationally. It then summarizes the methods of hyperspectral data acquisition and application scenarios. Subsequently, it organizes the processing of hyperspectral data for crop disease and pest detection and classification, deriving relevant preprocessing and analysis methods for hyperspectral data. Finally, it conducts a detailed analysis of classic cases using hyperspectral imaging technology for detecting crop diseases and pests and non-destructive classification, while also analyzing and summarizing the future development trends of hyperspectral imaging technology in agricultural production. The non-destructive rapid detection and classification technology of hyperspectral imaging can effectively select qualified crops and classify crops of different qualities, ensuring the quality of agricultural products. In conclusion, hyperspectral imaging technology can effectively serve the agricultural economy, making agricultural production more intelligent and holding significant importance for the development of agriculture in China.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/coatings14101285</doi><oa>free_for_read</oa></addata></record> |
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subjects | Accuracy Agricultural industry Agricultural production Agriculture Algorithms Artificial intelligence Classification Crop damage Crop diseases Crop production Cultural heritage Data acquisition Data analysis Data entry Deep learning Disease prevention Economics Hyperspectral imaging Identification Image acquisition Imaging systems Leaves Machine learning Medical imaging Methods Mold Nondestructive testing Pests Plant diseases Preprocessing Spectral methods Technology assessment Unmanned aerial vehicles |
title | A Review of the Application of Hyperspectral Imaging Technology in Agricultural Crop Economics |
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