TY - JOUR
T1 - Non-Invasive Computer Vision-Based Fruit Fly Larvae Differentiation
T2 - Ceratitis capitata and Bactrocera zonata
AU - Kanevsky, Eddie
AU - Lazebnik, Teddy
AU - Kaspi, Roy
AU - Gazit, Yoav
AU - Halon, Eyal
AU - Fried, Dror
AU - Zamansky, Anna
AU - Pines, Gur
N1 - Publisher Copyright:
© 2025 Wiley-VCH GmbH. Published by John Wiley & Sons Ltd.
PY - 2025/9/8
Y1 - 2025/9/8
N2 - The Mediterranean fruit fly (Ceratitis capitata) and the peach fruit fly (Bactrocera zonata) are two of the most economically significant agricultural pests affecting fruit production worldwide. Both are considered quarantine pests in several countries, which oblige the use of restrictive measures to assure safe trade with countries where these flies are present. As the quarantine status of these two pests is not similar in every country, discriminating measures among these two fruit flies' larvae in the exported fruits is critical for safe trade. Traditional DNA-based detection methods, though accurate, are costly and time-consuming, while manual morphological identification is practically impossible. In this study, we propose a novel non-invasive method utilising computer vision for rapid differentiation between larvae of these two species based on a short video recording of a single larva freely moving on a Petri dish. Our results reveal good separation between the two species with 90% accuracy using videos as short as 15 s long.
AB - The Mediterranean fruit fly (Ceratitis capitata) and the peach fruit fly (Bactrocera zonata) are two of the most economically significant agricultural pests affecting fruit production worldwide. Both are considered quarantine pests in several countries, which oblige the use of restrictive measures to assure safe trade with countries where these flies are present. As the quarantine status of these two pests is not similar in every country, discriminating measures among these two fruit flies' larvae in the exported fruits is critical for safe trade. Traditional DNA-based detection methods, though accurate, are costly and time-consuming, while manual morphological identification is practically impossible. In this study, we propose a novel non-invasive method utilising computer vision for rapid differentiation between larvae of these two species based on a short video recording of a single larva freely moving on a Petri dish. Our results reveal good separation between the two species with 90% accuracy using videos as short as 15 s long.
KW - AI for animals
KW - agricultural pest detection
KW - morphological species identification
KW - video-based analysis
UR - https://www.scopus.com/pages/publications/105015435780
U2 - 10.1111/jen.70009
DO - 10.1111/jen.70009
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AN - SCOPUS:105015435780
SN - 0931-2048
JO - Journal of Applied Entomology
JF - Journal of Applied Entomology
ER -