The AgriQ: A low-cost unmanned aerial system for precision agriculture
•The AgriQ, a low-cost and open-source unmanned aerial system, is presented.•The construction of a dual-spectrum system with two low-cost cameras is detailed.•Eight vegetation indices are computed with the visible and near-infrared data.•Detailed information for farmers is derived by analyzing the v...
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description | •The AgriQ, a low-cost and open-source unmanned aerial system, is presented.•The construction of a dual-spectrum system with two low-cost cameras is detailed.•Eight vegetation indices are computed with the visible and near-infrared data.•Detailed information for farmers is derived by analyzing the vegetation indices.•A comparison of the AgriQ with a commercial system shows outstanding results.
Precision agriculture currently presents significant growth thanks to the development of three main technologies: drones, vision-based systems, and embedded electronics. To take advantage of these technologies, services and products have launched cameras, drones, and algorithms in the cloud, from which the farmer can easily obtain valuable information for the optimization of their production. Notwithstanding the previous, in Latin America (and in general in the undeveloped countries), these technologies are costly and sometimes difficult to access for most farmers. This study proposes developing the low-cost Unmanned Aerial System (UAS) for precision agriculture tasks called AgriQ. It comprises three subsystems: (a) the drone; (b) the multispectral imaging system; and (c) the open-source software responsible for computing valuable information for the farmers. Intending to obtain a competitive UAS, we tackle the problem from four main points: (1) the construction of the drone; (2) the vision algorithms; (3) the autonomous trajectory considering all the parameters for properly recovering all the crops’ visual information; and (4) the construction of a low-cost multispectral imaging system with multiple bands. The multispectral imaging system is onboard the quadrotor and is composed of two low-cost cameras that have been modified to output multispectral imagery. With all this unmanned aerial system, we compute 8 vegetation indices that provide useful information to the farmers. To verify that the AgriQ is competitive versus commercial systems, we compared its performance to the professional multispectral camera RedEdge-M and popular Pix4D software. For this purpose, we have conducted experiments in two real environments, one of which is a real crop field. The experiments’ data show that our AgriQ UAS achieved prominent results at a fraction of the commercial system cost. Furthermore, to provide the user with a complete low-cost tool for precision agriculture, all the parts of the AgriQ are presented and explained in detail. The software is developed using open-source co |
doi_str_mv | 10.1016/j.eswa.2021.115163 |
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Precision agriculture currently presents significant growth thanks to the development of three main technologies: drones, vision-based systems, and embedded electronics. To take advantage of these technologies, services and products have launched cameras, drones, and algorithms in the cloud, from which the farmer can easily obtain valuable information for the optimization of their production. Notwithstanding the previous, in Latin America (and in general in the undeveloped countries), these technologies are costly and sometimes difficult to access for most farmers. This study proposes developing the low-cost Unmanned Aerial System (UAS) for precision agriculture tasks called AgriQ. It comprises three subsystems: (a) the drone; (b) the multispectral imaging system; and (c) the open-source software responsible for computing valuable information for the farmers. Intending to obtain a competitive UAS, we tackle the problem from four main points: (1) the construction of the drone; (2) the vision algorithms; (3) the autonomous trajectory considering all the parameters for properly recovering all the crops’ visual information; and (4) the construction of a low-cost multispectral imaging system with multiple bands. The multispectral imaging system is onboard the quadrotor and is composed of two low-cost cameras that have been modified to output multispectral imagery. With all this unmanned aerial system, we compute 8 vegetation indices that provide useful information to the farmers. To verify that the AgriQ is competitive versus commercial systems, we compared its performance to the professional multispectral camera RedEdge-M and popular Pix4D software. For this purpose, we have conducted experiments in two real environments, one of which is a real crop field. The experiments’ data show that our AgriQ UAS achieved prominent results at a fraction of the commercial system cost. Furthermore, to provide the user with a complete low-cost tool for precision agriculture, all the parts of the AgriQ are presented and explained in detail. The software is developed using open-source code where our contribution is available online on our GitHub site.</description><identifier>ISSN: 0957-4174</identifier><identifier>EISSN: 1873-6793</identifier><identifier>DOI: 10.1016/j.eswa.2021.115163</identifier><language>eng</language><publisher>New York: Elsevier Ltd</publisher><subject>Agriculture ; Algorithms ; Cameras ; Cloud computing ; Computer vision ; Drones ; Imaging ; Low cost ; Low-cost system ; Multispectral imaging ; Open source software ; Optimization ; Precision agriculture ; Software ; Source code ; Subsystems ; Unmanned aerial system ; Unmanned aerial vehicles ; Vision systems</subject><ispartof>Expert systems with applications, 2021-11, Vol.182, p.115163, Article 115163</ispartof><rights>2021 Elsevier Ltd</rights><rights>Copyright Elsevier BV Nov 15, 2021</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c328t-c9c9020784365f222c53da842f1cc321a74443099b2356899650490cbfa8eed63</citedby><cites>FETCH-LOGICAL-c328t-c9c9020784365f222c53da842f1cc321a74443099b2356899650490cbfa8eed63</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.eswa.2021.115163$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3549,27923,27924,45994</link.rule.ids></links><search><creatorcontrib>Montes de Oca, Andrés</creatorcontrib><creatorcontrib>Flores, Gerardo</creatorcontrib><title>The AgriQ: A low-cost unmanned aerial system for precision agriculture</title><title>Expert systems with applications</title><description>•The AgriQ, a low-cost and open-source unmanned aerial system, is presented.•The construction of a dual-spectrum system with two low-cost cameras is detailed.•Eight vegetation indices are computed with the visible and near-infrared data.•Detailed information for farmers is derived by analyzing the vegetation indices.•A comparison of the AgriQ with a commercial system shows outstanding results.
Precision agriculture currently presents significant growth thanks to the development of three main technologies: drones, vision-based systems, and embedded electronics. To take advantage of these technologies, services and products have launched cameras, drones, and algorithms in the cloud, from which the farmer can easily obtain valuable information for the optimization of their production. Notwithstanding the previous, in Latin America (and in general in the undeveloped countries), these technologies are costly and sometimes difficult to access for most farmers. This study proposes developing the low-cost Unmanned Aerial System (UAS) for precision agriculture tasks called AgriQ. It comprises three subsystems: (a) the drone; (b) the multispectral imaging system; and (c) the open-source software responsible for computing valuable information for the farmers. Intending to obtain a competitive UAS, we tackle the problem from four main points: (1) the construction of the drone; (2) the vision algorithms; (3) the autonomous trajectory considering all the parameters for properly recovering all the crops’ visual information; and (4) the construction of a low-cost multispectral imaging system with multiple bands. The multispectral imaging system is onboard the quadrotor and is composed of two low-cost cameras that have been modified to output multispectral imagery. With all this unmanned aerial system, we compute 8 vegetation indices that provide useful information to the farmers. To verify that the AgriQ is competitive versus commercial systems, we compared its performance to the professional multispectral camera RedEdge-M and popular Pix4D software. For this purpose, we have conducted experiments in two real environments, one of which is a real crop field. The experiments’ data show that our AgriQ UAS achieved prominent results at a fraction of the commercial system cost. Furthermore, to provide the user with a complete low-cost tool for precision agriculture, all the parts of the AgriQ are presented and explained in detail. The software is developed using open-source code where our contribution is available online on our GitHub site.</description><subject>Agriculture</subject><subject>Algorithms</subject><subject>Cameras</subject><subject>Cloud computing</subject><subject>Computer vision</subject><subject>Drones</subject><subject>Imaging</subject><subject>Low cost</subject><subject>Low-cost system</subject><subject>Multispectral imaging</subject><subject>Open source software</subject><subject>Optimization</subject><subject>Precision agriculture</subject><subject>Software</subject><subject>Source code</subject><subject>Subsystems</subject><subject>Unmanned aerial system</subject><subject>Unmanned aerial vehicles</subject><subject>Vision systems</subject><issn>0957-4174</issn><issn>1873-6793</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9kF1LwzAUhoMoOKd_wKuA1635aJNGvBnDqTAQYV6HLD3VlK6ZSevYvzejXntuzs37nI8HoVtKckqouG9ziAeTM8JoTmlJBT9DM1pJngmp-DmaEVXKrKCyuERXMbaEUEmInKHV5gvw4jO49we8wJ0_ZNbHAY_9zvQ91NhAcKbD8RgH2OHGB7wPYF10vscmYXbshjHANbpoTBfh5q_P0cfqabN8ydZvz6_LxTqznFVDZpVVhBFZFVyUDWPMlrw2VcEaalOCGlkUBSdKbRkvRaWUKEmhiN02pgKoBZ-ju2nuPvjvEeKgWz-GPq3UrJSCC5kqpdiUssHHGKDR--B2Jhw1JfrkS7f65EuffOnJV4IeJwjS_T8Ogo7WQW-hdunjQdfe_Yf_Aht0ccg</recordid><startdate>20211115</startdate><enddate>20211115</enddate><creator>Montes de Oca, Andrés</creator><creator>Flores, Gerardo</creator><general>Elsevier Ltd</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20211115</creationdate><title>The AgriQ: A low-cost unmanned aerial system for precision agriculture</title><author>Montes de Oca, Andrés ; Flores, Gerardo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c328t-c9c9020784365f222c53da842f1cc321a74443099b2356899650490cbfa8eed63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Agriculture</topic><topic>Algorithms</topic><topic>Cameras</topic><topic>Cloud computing</topic><topic>Computer vision</topic><topic>Drones</topic><topic>Imaging</topic><topic>Low cost</topic><topic>Low-cost system</topic><topic>Multispectral imaging</topic><topic>Open source software</topic><topic>Optimization</topic><topic>Precision agriculture</topic><topic>Software</topic><topic>Source code</topic><topic>Subsystems</topic><topic>Unmanned aerial system</topic><topic>Unmanned aerial vehicles</topic><topic>Vision systems</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Montes de Oca, Andrés</creatorcontrib><creatorcontrib>Flores, Gerardo</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Expert systems with applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Montes de Oca, Andrés</au><au>Flores, Gerardo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The AgriQ: A low-cost unmanned aerial system for precision agriculture</atitle><jtitle>Expert systems with applications</jtitle><date>2021-11-15</date><risdate>2021</risdate><volume>182</volume><spage>115163</spage><pages>115163-</pages><artnum>115163</artnum><issn>0957-4174</issn><eissn>1873-6793</eissn><abstract>•The AgriQ, a low-cost and open-source unmanned aerial system, is presented.•The construction of a dual-spectrum system with two low-cost cameras is detailed.•Eight vegetation indices are computed with the visible and near-infrared data.•Detailed information for farmers is derived by analyzing the vegetation indices.•A comparison of the AgriQ with a commercial system shows outstanding results.
Precision agriculture currently presents significant growth thanks to the development of three main technologies: drones, vision-based systems, and embedded electronics. To take advantage of these technologies, services and products have launched cameras, drones, and algorithms in the cloud, from which the farmer can easily obtain valuable information for the optimization of their production. Notwithstanding the previous, in Latin America (and in general in the undeveloped countries), these technologies are costly and sometimes difficult to access for most farmers. This study proposes developing the low-cost Unmanned Aerial System (UAS) for precision agriculture tasks called AgriQ. It comprises three subsystems: (a) the drone; (b) the multispectral imaging system; and (c) the open-source software responsible for computing valuable information for the farmers. Intending to obtain a competitive UAS, we tackle the problem from four main points: (1) the construction of the drone; (2) the vision algorithms; (3) the autonomous trajectory considering all the parameters for properly recovering all the crops’ visual information; and (4) the construction of a low-cost multispectral imaging system with multiple bands. The multispectral imaging system is onboard the quadrotor and is composed of two low-cost cameras that have been modified to output multispectral imagery. With all this unmanned aerial system, we compute 8 vegetation indices that provide useful information to the farmers. To verify that the AgriQ is competitive versus commercial systems, we compared its performance to the professional multispectral camera RedEdge-M and popular Pix4D software. For this purpose, we have conducted experiments in two real environments, one of which is a real crop field. The experiments’ data show that our AgriQ UAS achieved prominent results at a fraction of the commercial system cost. Furthermore, to provide the user with a complete low-cost tool for precision agriculture, all the parts of the AgriQ are presented and explained in detail. The software is developed using open-source code where our contribution is available online on our GitHub site.</abstract><cop>New York</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.eswa.2021.115163</doi></addata></record> |
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subjects | Agriculture Algorithms Cameras Cloud computing Computer vision Drones Imaging Low cost Low-cost system Multispectral imaging Open source software Optimization Precision agriculture Software Source code Subsystems Unmanned aerial system Unmanned aerial vehicles Vision systems |
title | The AgriQ: A low-cost unmanned aerial system for precision agriculture |
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