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Technical Note

DIRT: The Dacus Image Recognition Toolkit

Department of Informatics, Ionian University, 49132 Kerkyra, Greece
Creative Web Applications P.C., 49131 Kerkyra, Greece
Author to whom correspondence should be addressed.
J. Imaging 2018, 4(11), 129;
Received: 25 August 2018 / Revised: 25 October 2018 / Accepted: 26 October 2018 / Published: 30 October 2018
(This article belongs to the Special Issue Image Based Information Retrieval from the Web)
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” (DIRT), 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. View Full-Text
Keywords: object recognition; deep learning; Bactrocera Oleae; Dacus; olive fruit fly; smart-traps; public dataset; public API/web-service; IPM DSS; olive cultivation object recognition; deep learning; Bactrocera Oleae; Dacus; olive fruit fly; smart-traps; public dataset; public API/web-service; IPM DSS; olive cultivation
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MDPI and ACS Style

Kalamatianos, R.; Karydis, I.; Doukakis, D.; Avlonitis, M. DIRT: The Dacus Image Recognition Toolkit. J. Imaging 2018, 4, 129.

AMA Style

Kalamatianos R, Karydis I, Doukakis D, Avlonitis M. DIRT: The Dacus Image Recognition Toolkit. Journal of Imaging. 2018; 4(11):129.

Chicago/Turabian Style

Kalamatianos, Romanos, Ioannis Karydis, Dimitris Doukakis, and Markos Avlonitis. 2018. "DIRT: The Dacus Image Recognition Toolkit" Journal of Imaging 4, no. 11: 129.

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