Naslov
EfficientDet-4 Deep Neural Network-Based Remote Monitoring of Codling Moth Population for Early Damage Detection in Apple Orchard

Autori
Čirjak, Dana ; Aleksi, Ivan ; Lemic, Darija ; Pajač Živković, Ivana

Izvornik
Agriculture (2077-0472) 13(5) (2023), 961; 1-20

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
automatic monitoring system ; Cydia pomonella L. ; deep learning ; precision agriculture ; site-specific management ; smart trap

Sažetak
Deep neural networks (DNNs) have recently been applied in many areas of agriculture, including pest monitoring. The codling moth is the most damaging apple pest, and the currently available methods for its monitoring are outdated and time- consuming. Therefore, the aim of this study was to develop an automatic monitoring system for codling moth based on DNNs. The system consists of a smart trap and an analytical model. The smart trap enables data processing on-site and does not send the whole image to the user but only the detection results. Therefore, it does not consume much energy and is suitable for rural areas. For model development, a dataset of 430 sticky pad photos of codling moth was collected in three apple orchards. The photos were labelled, resulting in 8142 annotations of codling moths, 5458 of other insects, and 8177 of other objects. The results were statistically evaluated using the confusion matrix, and the developed model showed an accuracy > of 99% in detecting codling moths. This developed system contributes to automatic pest monitoring and sustainable apple production.

Izvorni jezik
Engleski

Znanstvena područja
Poljoprivreda (agronomija)

Napomena
Objavu rada podržao je Fond za otvoreni pristup
publikacijama Sveučilišta u Zagrebu Agronomskog
fakulteta i Europski fond za regionalni razvoj
kroz projekt AgriART sveobuhvatni upravljački
sustav u području precizne poljoprivrede
(KK.01.2.1.02.0290).

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