Plant disease detection using drones in precision agriculture
<div><p>Plant diseases affect the quality and quantity of agricultural products and have an impact on food safety. These effects result in a loss of income in the production sectors which are particularly critical for developing countries. Visual inspection by subject matter experts is t...
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| مؤلفون آخرون: | , |
| منشور في: |
2023
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| _version_ | 1864513533710958592 |
|---|---|
| author | Ruben Chin (17725986) |
| author2 | Cagatay Catal (6897842) Ayalew Kassahun (8849540) |
| author2_role | author author |
| author_facet | Ruben Chin (17725986) Cagatay Catal (6897842) Ayalew Kassahun (8849540) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ruben Chin (17725986) Cagatay Catal (6897842) Ayalew Kassahun (8849540) |
| dc.date.none.fl_str_mv | 2023-03-28T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s11119-023-10014-y |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Plant_disease_detection_using_drones_in_precision_agriculture/24934965 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Agricultural, veterinary and food sciences Agricultural biotechnology Biological sciences Other biological sciences Drone Plant disease detection Machine learning |
| dc.title.none.fl_str_mv | Plant disease detection using drones in precision agriculture |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <div><p>Plant diseases affect the quality and quantity of agricultural products and have an impact on food safety. These effects result in a loss of income in the production sectors which are particularly critical for developing countries. Visual inspection by subject matter experts is time-consuming, expensive and not scalable for large farms. As such, the automation of plant disease detection is a feasible solution to prevent losses in yield. Nowadays, one of the most popular approaches for this automation is to use drones. Though there are several articles published on the use of drones for plant disease detection, a systematic overview of these studies is lacking. To address this problem, a systematic literature review (SLR) on the use of drones for plant disease detection was undertaken and 38 primary studies were selected to answer research questions related to disease types, drone categories, stakeholders, machine learning tasks, data, techniques to support decision-making, agricultural product types and challenges. It was shown that the most common disease is blight; fungus is the most important pathogen and grape and watermelon are the most studied crops. The most used drone type is the quadcopter and the most applied machine learning task is classification. Color-infrared (CIR) images are the most preferred data used and field images are the main focus. The machine learning algorithm applied most is convolutional neural network (CNN). In addition, the challenges to pave the way for further research were provided.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Precision Agriculture<br> License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s11119-023-10014-y" target="_blank">https://dx.doi.org/10.1007/s11119-023-10014-y</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_485de6deb95b39111895e41422f6715d |
| identifier_str_mv | 10.1007/s11119-023-10014-y |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24934965 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Plant disease detection using drones in precision agricultureRuben Chin (17725986)Cagatay Catal (6897842)Ayalew Kassahun (8849540)Agricultural, veterinary and food sciencesAgricultural biotechnologyBiological sciencesOther biological sciencesDronePlant disease detectionMachine learning<div><p>Plant diseases affect the quality and quantity of agricultural products and have an impact on food safety. These effects result in a loss of income in the production sectors which are particularly critical for developing countries. Visual inspection by subject matter experts is time-consuming, expensive and not scalable for large farms. As such, the automation of plant disease detection is a feasible solution to prevent losses in yield. Nowadays, one of the most popular approaches for this automation is to use drones. Though there are several articles published on the use of drones for plant disease detection, a systematic overview of these studies is lacking. To address this problem, a systematic literature review (SLR) on the use of drones for plant disease detection was undertaken and 38 primary studies were selected to answer research questions related to disease types, drone categories, stakeholders, machine learning tasks, data, techniques to support decision-making, agricultural product types and challenges. It was shown that the most common disease is blight; fungus is the most important pathogen and grape and watermelon are the most studied crops. The most used drone type is the quadcopter and the most applied machine learning task is classification. Color-infrared (CIR) images are the most preferred data used and field images are the main focus. The machine learning algorithm applied most is convolutional neural network (CNN). In addition, the challenges to pave the way for further research were provided.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Precision Agriculture<br> License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s11119-023-10014-y" target="_blank">https://dx.doi.org/10.1007/s11119-023-10014-y</a></p>2023-03-28T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s11119-023-10014-yhttps://figshare.com/articles/journal_contribution/Plant_disease_detection_using_drones_in_precision_agriculture/24934965CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/249349652023-03-28T03:00:00Z |
| spellingShingle | Plant disease detection using drones in precision agriculture Ruben Chin (17725986) Agricultural, veterinary and food sciences Agricultural biotechnology Biological sciences Other biological sciences Drone Plant disease detection Machine learning |
| status_str | publishedVersion |
| title | Plant disease detection using drones in precision agriculture |
| title_full | Plant disease detection using drones in precision agriculture |
| title_fullStr | Plant disease detection using drones in precision agriculture |
| title_full_unstemmed | Plant disease detection using drones in precision agriculture |
| title_short | Plant disease detection using drones in precision agriculture |
| title_sort | Plant disease detection using drones in precision agriculture |
| topic | Agricultural, veterinary and food sciences Agricultural biotechnology Biological sciences Other biological sciences Drone Plant disease detection Machine learning |