YOLO deep learning algorithm for object detection in agriculture: a review
YOLO represents the one-stage object detection also called regression-based object detection. Object in the given input is directly classified and located instead of using the candidate region. The accuracy from two-stage detection is higher than one-stage detection where one-stage object detection...
Uložené v:
| Vydané v: | Journal of agricultural engineering (Pisa, Italy) Ročník 55; číslo 4 |
|---|---|
| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Bologna
PAGEPress Publications
01.01.2024
|
| Predmet: | |
| ISSN: | 1974-7071, 2239-6268 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | YOLO represents the one-stage object detection also called regression-based object detection. Object in the given input is directly classified and located instead of using the candidate region. The accuracy from two-stage detection is higher than one-stage detection where one-stage object detection speed is higher than two-stage object detection. YOLO has become popular because of its Detection accuracy, good generalization, open-source, and speed. YOLO boasts exceptional speed due to its approach of using regression problems for frame detection, eliminating the need for a complex pipeline. In agriculture, using remote sensing and drone technologies YOLO classifies and detects crops, diseases, and pests, and is also used for land use mapping, environmental monitoring, urban planning, and wildlife. Recent research highlights YOLO's impressive performance in various agricultural applications. For instance, YOLOv4 demonstrated high accuracy in counting and locating small objects in UAV-captured images of bean plants, achieving an AP of 84.8% and a recall of 89%. Similarly, YOLOv5 showed significant precision in identifying rice leaf diseases, with a precision rate of 90%. In this review, we discuss the basic principles behind YOLO, different versions of YOLO, limitations, and YOLO application in agriculture and farming. |
|---|---|
| AbstractList | YOLO represents the one-stage object detection also called regression-based object detection. Object in the given input is directly classified and located instead of using the candidate region. The accuracy from two-stage detection is higher than one-stage detection where one-stage object detection speed is higher than two-stage object detection. YOLO has become popular because of its Detection accuracy, good generalization, open-source, and speed. YOLO boasts exceptional speed due to its approach of using regression problems for frame detection, eliminating the need for a complex pipeline. In agriculture, using remote sensing and drone technologies YOLO classifies and detects crops, diseases, and pests, and is also used for land use mapping, environmental monitoring, urban planning, and wildlife. Recent research highlights YOLO's impressive performance in various agricultural applications. For instance, YOLOv4 demonstrated high accuracy in counting and locating small objects in UAV-captured images of bean plants, achieving an AP of 84.8% and a recall of 89%. Similarly, YOLOv5 showed significant precision in identifying rice leaf diseases, with a precision rate of 90%. In this review, we discuss the basic principles behind YOLO, different versions of YOLO, limitations, and YOLO application in agriculture and farming. YOLO represents the one-stage object detection also called regression-based object detection. Object in the given input is directly classified and located instead of using the candidate region. The accuracy from two-stage detection is higher than one-stage detection where one-stage object detection speed is higher than two-stage object detection. YOLO has become popular because of its Detection accuracy, good generalization, open-source, and speed. YOLO boasts exceptiol speed due to its approach of using regression problems for frame detection, elimiting the need for a complex pipeline. In agriculture, using remote sensing and drone technologies YOLO classifies and detects crops, diseases, and pests, and is also used for land use mapping, environmental monitoring, urban planning, and wildlife. Recent research highlights YOLO's impressive performance in various agricultural applications. For instance, YOLOv4 demonstrated high accuracy in counting and locating small objects in UAV-captured images of bean plants, achieving an AP of 84.8% and a recall of 89%. Similarly, YOLOv5 showed significant precision in identifying rice leaf diseases, with a precision rate of 90%. In this review, we discuss the basic principles behind YOLO, different versions of YOLO, limitations, and YOLO application in agriculture and farming. |
| Author | R, Jagadeeswaran P, Pazhanivelan Kanna S, Kamalesh P.C., Prabu Ramalingam, Kumaraperumal |
| Author_xml | – sequence: 1 givenname: Kamalesh surname: Kanna S fullname: Kanna S, Kamalesh – sequence: 2 givenname: Kumaraperumal surname: Ramalingam fullname: Ramalingam, Kumaraperumal – sequence: 3 givenname: Pazhanivelan surname: P fullname: P, Pazhanivelan – sequence: 4 givenname: Jagadeeswaran surname: R fullname: R, Jagadeeswaran – sequence: 5 givenname: Prabu surname: P.C. fullname: P.C., Prabu |
| BookMark | eNptkc1PGzEUxK0KpKbAsXdLPW_qr_iDG0KUgiLlQg89WW-9b4NXyzr1Oq347-sQuCBOIz3NjEbv94WcTGlCQr5ytlTM8u8D4FIwoZZcK_6JLISQrtFC2xOy4M6oxjDDP5OLeR4YY1w4Z5xckPvfm_WGdog7OiLkKU5bCuM25Vgen2ifMk3tgKFUS6kS00TjRGGbY9iPZZ_xkgLN-Dfiv3Ny2sM448WrnpFfP24ern82683t3fXVugnSsNLodqXAYheY612_ki5Ia0MrbBeMxqpobasdMKZ54NChgpVQtlOyV6x1nTwjd8feLsHgdzk-QX72CaJ_OaS89ZBLDCP6TgXdCr6qUaN02zsBvbK8dZYbZQWvXd-OXbuc_uxxLn5I-zzV-V5yI4VwSrPqkkdXyGmeM_Y-xAKHZ5QMcfSc-QMDXxn4AwN_YFBTzbvU29aP_f8BWRKJCQ |
| CitedBy_id | crossref_primary_10_3390_app15179341 crossref_primary_10_1016_j_biosystemseng_2025_104245 crossref_primary_10_3390_life15060910 crossref_primary_10_1016_j_atech_2025_101126 crossref_primary_10_3390_agriculture15161786 |
| Cites_doi | 10.1109/ICCV.1998.710772 10.3390/f15050737 10.1109/ITW.2015.7133169 10.1007/978-3-319-46475-6_28 10.1007/978-981-99-7216-6_18 10.1016/j.procs.2022.01.135 10.3390/app13126880 10.1109/CVPR.2016.91 10.3390/f15040691 10.3390/agriengineering5040111 10.3390/a15120440 10.18494/SAM3553 10.1109/ACCESS.2023.3343450 10.1109/TPAMI.2014.2343217 10.1109/WACV.2016.7477564 10.5039/agraria.v17i2a1353 10.14569/IJACSA.2023.0140265 10.1016/j.atech.2023.100311 10.1109/MetroAgriFor52389.2021.9628717 10.48084/etasr.6377 10.1155/2019/8597606 10.1016/j.compag.2020.105742 10.1007/978-981-99-6755-1_17 10.1109/ACCESS.2018.2834960 10.1109/ICIIS58898.2023.10253539 10.1109/ICCV48922.2021.00349 10.3390/app13148502 10.1016/j.ophoto.2023.100045 10.1109/CVPR.2017.690 10.1109/CVPR46437.2021.01352 10.1590/1678-992x-2022-0064 10.1109/CVPR52729.2023.00721 10.3389/frobt.2021.627009 10.5391/IJFIS.2022.22.3.223 10.1007/s11042-022-13644-y 10.3390/f15010061 10.3390/drones7080492 10.1007/978-3-031-45438-7_26 10.3390/s23042165 10.1016/j.compag.2021.106560 10.1109/EECSI59885.2023.10295670 10.19101/IJATEE.2022.10100136 10.3390/s20174938 10.3390/rs13245182 10.1109/ASYU56188.2022.9925332 10.1007/s10346-021-01694-6 10.5194/isprs-archives-XLVIII-4-W6-2022-75-2023 10.13031/trans.13791 10.1109/ICDAR.2015.7333881 10.1007/s42979-023-02572-9 10.1016/j.compag.2022.107116 10.3390/rs13112171 10.3389/fpls.2021.753603 10.1007/s11042-020-08976-6 10.1016/B978-0-12-818366-3.00005-8 10.3390/rs14236137 10.1109/ACCESS.2023.3325747 10.3390/a16070343 10.1007/978-3-319-46448-0_2 10.1109/CVPR.2018.00062 10.4108/eai.9-6-2022.174181 10.1109/MED51440.2021.9480344 10.1016/j.eswa.2023.122212 10.1109/ACCESS.2023.3292530 10.1109/CVPRW50498.2020.00203 10.1109/CVPR.2017.685 10.1109/ICCV.2015.169 10.3390/agronomy13082106 10.3390/f15050869 10.1088/1755-1315/974/1/012058 10.1007/978-981-99-7962-2_39 10.1177/1550147719852036 10.1007/978-3-031-72751-1_1 10.1016/j.atech.2023.100231 10.12785/ijcds/140199 10.7717/peerj-cs.1463 10.3390/s20236896 10.3389/fpls.2022.921057 10.1016/j.ecoinf.2022.101919 10.3390/electronics13020273 10.1109/ACCESS.2019.2933060 10.1109/ECICE50847.2020.9301932 10.1038/s41598-021-81216-5 10.3390/agronomy14010126 10.47163/agrociencia.v57i7.2970 10.1109/ACCESS.2024.3351805 10.3390/horticulturae9040443 10.1007/s00521-021-06029-z 10.5220/0009421302080215 10.3390/agronomy12102483 10.3390/agronomy13092279 10.1109/TPAMI.2014.2300479 10.1109/ACCESS.2022.3220234 10.1080/00401706.2021.1904738 10.3389/fpls.2023.1120724 10.1016/j.compag.2019.01.012 10.1109/CVPR.2018.00913 10.3390/rs15020539 10.1016/j.compag.2021.106066 10.1007/s11760-021-02024-y 10.1109/JSTARS.2022.3140776 10.3389/fpls.2022.806878 |
| ContentType | Journal Article |
| Copyright | 2024. This work is licensed under https://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: 2024. This work is licensed under https://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | AAYXX CITATION 3V. 7X2 8FE 8FG 8FH 8FK ABJCF ABUWG AEUYN AFKRA ATCPS AZQEC BENPR BGLVJ BHPHI CCPQU DWQXO HCIFZ L6V M0K M7S PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PTHSS DOA |
| DOI | 10.4081/jae.2024.1641 |
| DatabaseName | CrossRef ProQuest Central (Corporate) Agricultural Science Collection ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection ProQuest Central (Alumni) (purchase pre-March 2016) Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland Agricultural & Environmental Science Collection ProQuest Central Essentials ProQuest Central Technology Collection Natural Science Collection ProQuest One Community College ProQuest Central SciTech Premium Collection ProQuest Engineering Collection Agricultural Science Database Engineering Database ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition Engineering Collection DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef Agricultural Science Database Publicly Available Content Database Technology Collection ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Natural Science Collection ProQuest Central ProQuest One Applied & Life Sciences ProQuest One Sustainability ProQuest Engineering Collection Natural Science Collection ProQuest Central Korea Agricultural & Environmental Science Collection ProQuest Central (New) Engineering Collection Engineering Database ProQuest One Academic Eastern Edition Agricultural Science Collection ProQuest Technology Collection ProQuest SciTech Collection ProQuest One Academic UKI Edition Materials Science & Engineering Collection ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) |
| DatabaseTitleList | CrossRef Agricultural Science Database |
| Database_xml | – sequence: 1 dbid: DOA name: Directory of open access journals (DOAJ) url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Agriculture |
| EISSN | 2239-6268 |
| ExternalDocumentID | oai_doaj_org_article_d4c6b2153f4746bf92af481b98174821 10_4081_jae_2024_1641 |
| GroupedDBID | 5VS 67V 7X2 AAYXX ABDBF ABJCF ACUHS ADBBV AEUYN AFFHD AFKRA ALMA_UNASSIGNED_HOLDINGS ATCPS BCNDV BENPR BGLVJ BHPHI CCPQU CITATION GROUPED_DOAJ HCIFZ KQ8 M0K M7S PHGZM PHGZT PIMPY PQGLB PTHSS 3V. 8FE 8FG 8FH 8FK ABUWG AZQEC DWQXO L6V PKEHL PQEST PQQKQ PQUKI |
| ID | FETCH-LOGICAL-c370t-6b54a8edc09f9f539c388cb28dc76eb28e88b69a0061c1ade4a5248d43f40b9d3 |
| IEDL.DBID | M7S |
| ISICitedReferencesCount | 10 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001404714500009&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1974-7071 |
| IngestDate | Mon Nov 10 04:29:00 EST 2025 Fri Jul 25 11:43:27 EDT 2025 Sat Nov 29 03:38:47 EST 2025 Tue Nov 18 22:14:42 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Language | English |
| License | https://creativecommons.org/licenses/by-nc/4.0 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c370t-6b54a8edc09f9f539c388cb28dc76eb28e88b69a0061c1ade4a5248d43f40b9d3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| OpenAccessLink | https://www.proquest.com/docview/3173229460?pq-origsite=%requestingapplication% |
| PQID | 3173229460 |
| PQPubID | 4728908 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_d4c6b2153f4746bf92af481b98174821 proquest_journals_3173229460 crossref_citationtrail_10_4081_jae_2024_1641 crossref_primary_10_4081_jae_2024_1641 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-01-01 |
| PublicationDateYYYYMMDD | 2024-01-01 |
| PublicationDate_xml | – month: 01 year: 2024 text: 2024-01-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Bologna |
| PublicationPlace_xml | – name: Bologna |
| PublicationTitle | Journal of agricultural engineering (Pisa, Italy) |
| PublicationYear | 2024 |
| Publisher | PAGEPress Publications |
| Publisher_xml | – name: PAGEPress Publications |
| References | 20214 20213 20179 20212 20178 20211 20177 20210 20176 20175 20174 20219 20218 20217 20216 20215 20173 20172 20171 20170 20225 20224 20223 20189 20222 20188 20221 20187 20220 20186 20185 20229 20228 20227 20226 20184 20183 20182 20181 20180 20159 20158 20157 20156 20155 20154 20153 20152 20151 20150 20203 20169 20202 20168 20201 20167 20200 20166 20165 20164 20163 20209 20208 20207 20206 20205 20204 20162 20161 20160 20137 20136 20135 20134 20133 20139 20138 20148 20147 20146 20145 20144 20143 20142 20141 20149 20140 20236 20235 20234 20233 20199 20232 20198 20231 20197 20230 20196 20239 20238 20237 20195 20194 20193 20192 20191 20190 20247 20246 20245 20244 20243 20242 20241 20240 20248 |
| References_xml | – ident: 20197 doi: 10.1109/ICCV.1998.710772 – ident: 20236 doi: 10.3390/f15050737 – ident: 20221 doi: 10.1109/ITW.2015.7133169 – ident: 20243 doi: 10.1007/978-3-319-46475-6_28 – ident: 20215 – ident: 20133 doi: 10.1007/978-981-99-7216-6_18 – ident: 20176 doi: 10.1016/j.procs.2022.01.135 – ident: 20165 doi: 10.3390/app13126880 – ident: 20204 doi: 10.1109/CVPR.2016.91 – ident: 20248 doi: 10.3390/f15040691 – ident: 20170 doi: 10.3390/agriengineering5040111 – ident: 20216 doi: 10.3390/a15120440 – ident: 20153 doi: 10.18494/SAM3553 – ident: 20188 doi: 10.1109/ACCESS.2023.3343450 – ident: 20211 doi: 10.1109/TPAMI.2014.2343217 – ident: 20218 – ident: 20175 – ident: 20181 – ident: 20147 doi: 10.1109/WACV.2016.7477564 – ident: 20231 doi: 10.5039/agraria.v17i2a1353 – ident: 20191 doi: 10.14569/IJACSA.2023.0140265 – ident: 20179 doi: 10.1016/j.atech.2023.100311 – ident: 20195 doi: 10.1109/MetroAgriFor52389.2021.9628717 – ident: 20203 doi: 10.48084/etasr.6377 – ident: 20217 doi: 10.1155/2019/8597606 – ident: 20232 doi: 10.1016/j.compag.2020.105742 – ident: 20136 doi: 10.1007/978-981-99-6755-1_17 – ident: 20238 doi: 10.1109/ACCESS.2018.2834960 – ident: 20135 doi: 10.1109/ICIIS58898.2023.10253539 – ident: 20164 doi: 10.1109/ICCV48922.2021.00349 – ident: 20212 doi: 10.3390/app13148502 – ident: 20214 doi: 10.1016/j.ophoto.2023.100045 – ident: 20205 doi: 10.1109/CVPR.2017.690 – ident: 20159 doi: 10.1109/CVPR46437.2021.01352 – ident: 20213 – ident: 20144 doi: 10.1590/1678-992x-2022-0064 – ident: 20224 doi: 10.1109/CVPR52729.2023.00721 – ident: 20173 doi: 10.3389/frobt.2021.627009 – ident: 20193 doi: 10.5391/IJFIS.2022.22.3.223 – ident: 20160 doi: 10.1007/s11042-022-13644-y – ident: 20235 doi: 10.3390/f15010061 – ident: 20199 doi: 10.3390/drones7080492 – ident: 20149 doi: 10.1007/978-3-031-45438-7_26 – ident: 20157 doi: 10.3390/s23042165 – ident: 20177 doi: 10.1016/j.compag.2021.106560 – ident: 20140 doi: 10.1109/EECSI59885.2023.10295670 – ident: 20142 doi: 10.19101/IJATEE.2022.10100136 – ident: 20182 doi: 10.3390/s20174938 – ident: 20163 doi: 10.3390/rs13245182 – ident: 20196 doi: 10.1109/ASYU56188.2022.9925332 – ident: 20198 – ident: 20155 doi: 10.1007/s10346-021-01694-6 – ident: 20145 doi: 10.5194/isprs-archives-XLVIII-4-W6-2022-75-2023 – ident: 20171 doi: 10.13031/trans.13791 – ident: 20245 doi: 10.1109/ICDAR.2015.7333881 – ident: 20168 – ident: 20208 doi: 10.1007/s42979-023-02572-9 – ident: 20150 doi: 10.1016/j.compag.2022.107116 – ident: 20201 doi: 10.3390/rs13112171 – ident: 20219 – ident: 20200 doi: 10.3389/fpls.2021.753603 – ident: 20234 doi: 10.1007/s11042-020-08976-6 – ident: 20178 doi: 10.1016/B978-0-12-818366-3.00005-8 – ident: 20137 doi: 10.3390/rs14236137 – ident: 20187 doi: 10.1109/ACCESS.2023.3325747 – ident: 20189 doi: 10.3390/a16070343 – ident: 20185 doi: 10.1007/978-3-319-46448-0_2 – ident: 20246 doi: 10.1109/CVPR.2018.00062 – ident: 20242 doi: 10.4108/eai.9-6-2022.174181 – ident: 20183 doi: 10.1109/MED51440.2021.9480344 – ident: 20230 doi: 10.1016/j.eswa.2023.122212 – ident: 20240 doi: 10.1109/ACCESS.2023.3292530 – ident: 20225 doi: 10.1109/CVPRW50498.2020.00203 – ident: 20174 doi: 10.1109/CVPR.2017.685 – ident: 20228 – ident: 20169 doi: 10.1109/ICCV.2015.169 – ident: 20227 doi: 10.3390/agronomy13082106 – ident: 20233 doi: 10.3390/f15050869 – ident: 20194 doi: 10.1088/1755-1315/974/1/012058 – ident: 20209 doi: 10.1007/978-981-99-7962-2_39 – ident: 20239 doi: 10.1177/1550147719852036 – ident: 20226 doi: 10.1007/978-3-031-72751-1_1 – ident: 20134 doi: 10.1016/j.atech.2023.100231 – ident: 20141 doi: 10.12785/ijcds/140199 – ident: 20139 doi: 10.7717/peerj-cs.1463 – ident: 20148 doi: 10.3390/s20236896 – ident: 20158 doi: 10.3389/fpls.2022.921057 – ident: 20207 doi: 10.1016/j.ecoinf.2022.101919 – ident: 20237 doi: 10.3390/electronics13020273 – ident: 20156 doi: 10.1109/ACCESS.2019.2933060 – ident: 20210 doi: 10.1109/ECICE50847.2020.9301932 – ident: 20180 doi: 10.1038/s41598-021-81216-5 – ident: 20192 doi: 10.3390/agronomy14010126 – ident: 20172 – ident: 20162 doi: 10.47163/agrociencia.v57i7.2970 – ident: 20202 doi: 10.1109/ACCESS.2024.3351805 – ident: 20151 doi: 10.3390/horticulturae9040443 – ident: 20166 doi: 10.1007/s00521-021-06029-z – ident: 20222 doi: 10.5220/0009421302080215 – ident: 20186 – ident: 20154 doi: 10.3390/agronomy12102483 – ident: 20229 doi: 10.3390/agronomy13092279 – ident: 20161 doi: 10.1109/TPAMI.2014.2300479 – ident: 20146 – ident: 20138 doi: 10.1109/ACCESS.2022.3220234 – ident: 20152 doi: 10.1080/00401706.2021.1904738 – ident: 20247 doi: 10.3389/fpls.2023.1120724 – ident: 20220 doi: 10.1016/j.compag.2019.01.012 – ident: 20184 doi: 10.1109/CVPR.2018.00913 – ident: 20167 doi: 10.3390/rs15020539 – ident: 20143 doi: 10.1016/j.compag.2021.106066 – ident: 20190 doi: 10.1007/s11760-021-02024-y – ident: 20206 – ident: 20223 – ident: 20241 doi: 10.1109/JSTARS.2022.3140776 – ident: 20244 doi: 10.3389/fpls.2022.806878 |
| SSID | ssj0001299793 ssib055683572 ssib044743255 |
| Score | 2.3226922 |
| SecondaryResourceType | review_article |
| Snippet | YOLO represents the one-stage object detection also called regression-based object detection. Object in the given input is directly classified and located... |
| SourceID | doaj proquest crossref |
| SourceType | Open Website Aggregation Database Enrichment Source Index Database |
| SubjectTerms | Accuracy Agriculture Algorithms computer vision Deep learning Environmental monitoring Land use Machine learning object detection Object recognition Pests Plant diseases real-time farming Remote sensing Urban planning Wildlife YOLO |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LS8NAEF6keNCD-MRqlT2IJ2PT7CbZ9VbFIiKtB4V6WvaVWqlpaaO_39lk-xARL17DkCxfZne-GXa-QeiMMm6VUWkgmSEBjRQJVGazAMg4J7HLQpQuh02k3S7r9_njyqgvdyeskgeugGsaqhMFcYlkNKWJyngkMwpcizPg0qxsIY_ClK8kU-BJFGxJtOy4dDJbJPYdor76wtNSkbfF3YVECLSVACeFENl8k05AM6KXkEu0vgWsUtf_x7FdxqLONtryJBK3q8XvoDWb76LN9mDqhTTsHrp_6T30sLF2gv1giAGWo8F4Oixe3zEwVTxWrgQDJkV5GyvHwxzL5SuusMRVX8s-eu7cPt3cBX5uQqBJGhZBomIqmTU65BnPYsI1YUyriBmdJpBJM8uYSrh09EW3pLFUxhFlhgLGoeKGHKBaPs7tIcJAX1SSSBk7naLQZJBeKaaAcrSMkiRldXQxB0doLyruZluMBCQXDksBWAqHpXBY1tH5wnxSqWn8ZnjtkF4YORHs8gG4hvCuIf5yjTpqzP-T8DtzJoAvwRnGaRIe_cc3jtGGW3RVlGmgWjH9sCdoXX8Ww9n0tHTKL6YT3uw priority: 102 providerName: Directory of Open Access Journals |
| Title | YOLO deep learning algorithm for object detection in agriculture: a review |
| URI | https://www.proquest.com/docview/3173229460 https://doaj.org/article/d4c6b2153f4746bf92af481b98174821 |
| Volume | 55 |
| WOSCitedRecordID | wos001404714500009&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: Directory of open access journals (DOAJ) customDbUrl: eissn: 2239-6268 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001299793 issn: 1974-7071 databaseCode: DOA dateStart: 20070101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2239-6268 dateEnd: 99991231 omitProxy: false ssIdentifier: ssib044743255 issn: 1974-7071 databaseCode: M~E dateStart: 20120101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Agricultural Science Database customDbUrl: eissn: 2239-6268 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001299793 issn: 1974-7071 databaseCode: M0K dateStart: 20090601 isFulltext: true titleUrlDefault: https://search.proquest.com/agriculturejournals providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 2239-6268 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001299793 issn: 1974-7071 databaseCode: M7S dateStart: 20090601 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: Proquest Central customDbUrl: eissn: 2239-6268 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001299793 issn: 1974-7071 databaseCode: BENPR dateStart: 20090601 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2239-6268 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001299793 issn: 1974-7071 databaseCode: PIMPY dateStart: 20090601 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LT9wwELYK9EAPpbxUCl35gDgRSGInsXtBgEB9wLIqVIKT5Ve2W9Hd7W7K72fG8bKqqnLpJYdkZDmZseebyfgbQna5kN44UyVaOJbw3LDE1L5OAIxLVmAUYmxoNlF1u-L2VvZiwm0ayypne2LYqN3IYo78EPwc2J7kZXo0_pVg1yj8uxpbaCyQJWRJyELp3vXMnjgH95jPz10i2RYr4jnRmIORVeDlzSSWJYK7bWk4OTjKwx8aaTRzfgARRfaH2wrs_n9t3sEjna_877u8Ia8jFqXHrfGskheDZo28Ou5PIh-HXyef764urqjzfkxjf4k-1fd9GKz5_pMC4KUjg5kcEGlCUdeQDoZUz4f4QDVtj8dskG_nZzenH5PYfiGxrEqbpDQF18I7m8pa1gWTlglhTS6crUoIyIUXwpRSIwqymXae6yLnwnFW89RIxzbJ4nA09G8JBRRkylLrAumOUldDlGaEAeSSOaNZJbbI_uzrKhu5ybFFxr2CGAWVoUAZCpWhUBlbZO9JfNyScvxL8ARV9SSEXNrhxmjSV3FpKsdtacB6YNoVL00tc11zQPNSQLQmchhkZ6ZFFRf4VM1V-O75x9tkGafTZm12yGIz-e3fk5f2oRlMJx2ydHLW7X3thFQAXC_TL51gw_Ck9-myd_cISEzzHg |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEB6VFAk48EYUCuwBOOE28a7tXSSEyqNqaJrmUKT2tOzLIagkITEg_hS_kRl73QghuPXA1R6t1p7xzHzjnW8AHgupgvW2SIz0PBGp5YktQ5lgMq54RijEunrYRDEcyuNjNVqDn20vDB2rbH1i7aj9zFGNfBvjHNqeEnn35fxLQlOj6O9qO0KjMYv98OM7Qrbli_4b1O-TNN19e_R6L4lTBRLHi26V5DYTRgbvuqpUZcaV41I6m0rvihxxpgxS2lwZCu6uZ3wQJkuF9IKXomuV57juBVgXZOwdWB_1D0YnrQULgQE5XXV6Er0Xz2Jnaqz6qKJmAu4pOgiJAb4h_hQYmrc_GSLuTMUWYpjeb4GynifwR7ioY-Dutf_t7V2HqzHbZjvN53ED1ibVTbiyM15ExpFwC96dHA4OmQ9hzuIEjTEzp2PcfPXxM8OUns0s1apQpKqPrU3ZZMrMaonnzLCmAeg2vD-Xh7kDnelsGu4CwzzP5rkxGRE6dX2JONRKi7lZz1vDC7kBz1ptahfZ12kIyKlGFEbK16h8TcrXpPwNeHomPm9oR_4m-IpM40yI2MLrC7PFWEfno71wucXcDrddiNyWKjWlQLyiJOJRmeIim63V6OjClnplMvf-ffsRXNo7OhjoQX-4fx8u09aaGtUmdKrF1_AALrpv1WS5eBi_FgYfztvEfgH6oU1s |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=YOLO+deep+learning+algorithm+for+object+detection+in+agriculture%3A+a+review&rft.jtitle=Journal+of+agricultural+engineering+%28Pisa%2C+Italy%29&rft.au=Kanna+S%2C+Kamalesh&rft.au=Ramalingam%2C+Kumaraperumal&rft.au=P%2C+Pazhanivelan&rft.au=R%2C+Jagadeeswaran&rft.date=2024-01-01&rft.issn=1974-7071&rft.eissn=2239-6268&rft.volume=55&rft.issue=4&rft_id=info:doi/10.4081%2Fjae.2024.1641&rft.externalDBID=n%2Fa&rft.externalDocID=10_4081_jae_2024_1641 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1974-7071&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1974-7071&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1974-7071&client=summon |