Abstract
Selective thinning is a crucial operation to reduce forest ignitable material, to control the eucalyptus species and maximise its profitability. The selection and removal of less vigorous stems allows the remaining stems to grow healthier and without competition for water, sunlight and nutrients. This operation is traditionally performed by a human operator and is time-intensive. This work simplifies selective thinning by removing the stem selection part from the human operator’s side using a computer vision algorithm. For this, two distinct datasets of eucalyptus stems (with and without foliage) were built and manually annotated, and three Deep Learning object detectors (YOLOv5, YOLOv7 and YOLOv8) were tested on real context images to perform instance segmentation. YOLOv8 was the best at this task, achieving an Average Precision of 74% and 66% on non-leafy and leafy test datasets, respectively. A computer vision algorithm for automatic stem selection was developed based on the YOLOv8 segmentation output. The algorithm managed to get a Precision above 97% and a 81% Recall. The findings of this work can have a positive impact in future developments for automatising selective thinning in forested contexts.
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Acknowledgment
This work is financed by National Funds through the Portuguese funding agency, FCT—Fundação para a Ciência e a Tecnologia, within project LA/P/0063/2020. This work is also financed by the ERDF-European Regional Development Fund, through the Operational Programme for Competitiveness and Internationalisation-COMPETE 2020 Programme under the Portugal 2020 Partnership Agreement, within project SMARTCUT, with reference POCI-01-0247-FEDER-048183. Daniel Queirós da Silva thanks the FCT-Foundation for Science and Technology, Portugal for the Ph.D. Grant UI/BD/152564/2022.
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da Silva, D.Q., Rodrigues, T.F., Sousa, A.J., dos Santos, F.N., Filipe, V. (2023). Deep Learning-Based Tree Stem Segmentation for Robotic Eucalyptus Selective Thinning Operations. In: Moniz, N., Vale, Z., Cascalho, J., Silva, C., Sebastião, R. (eds) Progress in Artificial Intelligence. EPIA 2023. Lecture Notes in Computer Science(), vol 14116. Springer, Cham. https://doi.org/10.1007/978-3-031-49011-8_30
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