Abstract
This study compares two computer vision methods to detect yellow sticky traps using unmanned autonomous vehicles in olive tree cultivation. The traps aim to combat and monitor the density of the Bactrocera oleae, an important pest that damages olive fruit, leading to substantial economic losses annually. The evaluation encompassed two distinct methods: firstly, an algorithm employing conventional segmentation techniques like thresholding and contour localization, and secondly, a contemporary artificial intelligence approach utilizing YOLOv8, a state-of-the-art technology. A specific dataset was created to train and adjust the two algorithms. At the end of the study, both were able to locate the trap precisely. The segmentation algorithm demonstrated superior performance at proximal distances (50 cm), outperforming the outcomes achieved by YOLOv8. In contrast, YOLOv8 exhibited sustained precision, irrespective of the distance under examination. These findings affirm the versatility of both algorithms, highlighting their adaptability to various contexts based on distinct application demands. Consideration of trade-offs between accuracy and processing speed is essential in determining the most appropriate algorithm for a given application.
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https://github.com/ultralytics/ultralytics Accessed on July 7, 2023.
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Acknowledgements
This work has been supported by: SmartHealth - Inteligência Artificial para Cuidados de Saúde Personalizados ao Longo da Vida, under the project ref. NORTE-01-0145-FEDER-000045; OleaChain: Competências para a sustentabilidade e inovação da cadeia de valor do olival tradicional no Norte Interior de Portugal, NORTE-06-3559-FSE-000188, an operation to hire highly qualified human resources, funded by NORTE 2020 through the European Social Fund (ESF). The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020), ALGORITMI (UIDB/00319/2020) and SusTEC (LA /P/0007/2021). The authors thank Marta Sofia Madureira from the Agrobio Tecnologia - Insects Laboratory, part of the Mountain Research Center (CIMO), for the technical support provided throughout this work.
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Mendes, J., S. Berger, G., Lima, J., Costa, L., I. Pereira, A. (2024). Pest Management in Olive Cultivation Through Computer Vision: A Comparative Study of Detection Methods for Yellow Sticky Traps. In: Marques, L., Santos, C., Lima, J.L., Tardioli, D., Ferre, M. (eds) Robot 2023: Sixth Iberian Robotics Conference. ROBOT 2023. Lecture Notes in Networks and Systems, vol 978. Springer, Cham. https://doi.org/10.1007/978-3-031-59167-9_31
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