DIRT: The Dacus Image Recognition Toolkit
Modern agriculture is facing unique challenges in building a sustainable future for food production, in which the reliable detection of plantation threats is of critical importance. The breadth of existing information sources, and their equivalent sensors, can provide a wealth of data which, to be u...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-10-01
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| Series: | Journal of Imaging |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-433X/4/11/129 |
| _version_ | 1819320329590276096 |
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| author | Romanos Kalamatianos Ioannis Karydis Dimitris Doukakis Markos Avlonitis |
| author_facet | Romanos Kalamatianos Ioannis Karydis Dimitris Doukakis Markos Avlonitis |
| author_sort | Romanos Kalamatianos |
| collection | DOAJ |
| description | Modern agriculture is facing unique challenges in building a sustainable future for food production, in which the reliable detection of plantation threats is of critical importance. The breadth of existing information sources, and their equivalent sensors, can provide a wealth of data which, to be useful, must be transformed into actionable knowledge. Approaches based on Information Communication Technologies (ICT) have been shown to be able to help farmers and related stakeholders make decisions on problems by examining large volumes of data while assessing multiple criteria. In this paper, we address the automated identification (and count the instances) of the major threat of olive trees and their fruit, the Bactrocera Oleae (a.k.a. Dacus) based on images of the commonly used McPhail trap’s contents. Accordingly, we introduce the “Dacus Image Recognition Toolkit„ (<i>DIRT</i>), a collection of publicly available data, programming code samples and web-services focused at supporting research aiming at the management the Dacus as well as extensive experimentation on the capability of the proposed dataset in identifying Dacuses using Deep Learning methods. Experimental results indicated performance accuracy (mAP) of 91.52% in identifying Dacuses in trap images featuring various pests. Moreover, the results also indicated a trade-off between image attributes affecting detail, file size and complexity of approaches and mAP performance that can be selectively used to better tackle the needs of each usage scenario. |
| first_indexed | 2024-12-24T11:17:51Z |
| format | Article |
| id | doaj.art-a65ae78935c2468f8d68f0f911d635e8 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-12-24T11:17:51Z |
| publishDate | 2018-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-a65ae78935c2468f8d68f0f911d635e82022-12-21T16:58:20ZengMDPI AGJournal of Imaging2313-433X2018-10-0141112910.3390/jimaging4110129jimaging4110129DIRT: The Dacus Image Recognition ToolkitRomanos Kalamatianos0Ioannis Karydis1Dimitris Doukakis2Markos Avlonitis3Department of Informatics, Ionian University, 49132 Kerkyra, GreeceDepartment of Informatics, Ionian University, 49132 Kerkyra, GreeceDepartment of Informatics, Ionian University, 49132 Kerkyra, GreeceDepartment of Informatics, Ionian University, 49132 Kerkyra, GreeceModern agriculture is facing unique challenges in building a sustainable future for food production, in which the reliable detection of plantation threats is of critical importance. The breadth of existing information sources, and their equivalent sensors, can provide a wealth of data which, to be useful, must be transformed into actionable knowledge. Approaches based on Information Communication Technologies (ICT) have been shown to be able to help farmers and related stakeholders make decisions on problems by examining large volumes of data while assessing multiple criteria. In this paper, we address the automated identification (and count the instances) of the major threat of olive trees and their fruit, the Bactrocera Oleae (a.k.a. Dacus) based on images of the commonly used McPhail trap’s contents. Accordingly, we introduce the “Dacus Image Recognition Toolkit„ (<i>DIRT</i>), a collection of publicly available data, programming code samples and web-services focused at supporting research aiming at the management the Dacus as well as extensive experimentation on the capability of the proposed dataset in identifying Dacuses using Deep Learning methods. Experimental results indicated performance accuracy (mAP) of 91.52% in identifying Dacuses in trap images featuring various pests. Moreover, the results also indicated a trade-off between image attributes affecting detail, file size and complexity of approaches and mAP performance that can be selectively used to better tackle the needs of each usage scenario.https://www.mdpi.com/2313-433X/4/11/129object recognitiondeep learningBactrocera OleaeDacusolive fruit flysmart-trapspublic datasetpublic API/web-serviceIPM DSSolive cultivation |
| spellingShingle | Romanos Kalamatianos Ioannis Karydis Dimitris Doukakis Markos Avlonitis DIRT: The Dacus Image Recognition Toolkit Journal of Imaging object recognition deep learning Bactrocera Oleae Dacus olive fruit fly smart-traps public dataset public API/web-service IPM DSS olive cultivation |
| title | DIRT: The Dacus Image Recognition Toolkit |
| title_full | DIRT: The Dacus Image Recognition Toolkit |
| title_fullStr | DIRT: The Dacus Image Recognition Toolkit |
| title_full_unstemmed | DIRT: The Dacus Image Recognition Toolkit |
| title_short | DIRT: The Dacus Image Recognition Toolkit |
| title_sort | dirt the dacus image recognition toolkit |
| topic | object recognition deep learning Bactrocera Oleae Dacus olive fruit fly smart-traps public dataset public API/web-service IPM DSS olive cultivation |
| url | https://www.mdpi.com/2313-433X/4/11/129 |
| work_keys_str_mv | AT romanoskalamatianos dirtthedacusimagerecognitiontoolkit AT ioanniskarydis dirtthedacusimagerecognitiontoolkit AT dimitrisdoukakis dirtthedacusimagerecognitiontoolkit AT markosavlonitis dirtthedacusimagerecognitiontoolkit |