Cherry Tree Crown Extraction Using Machine Learning Based on Images from UAVs

Vasileios Moysiadis, Ilias Siniosoglou, Georgios Kokkonis, Vasileios Argyriou, Thomas Lagkas, Sotirios K. Goudos and Panagiotis Sarigiannidis ()
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Vasileios Moysiadis: Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece
Ilias Siniosoglou: Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece
Georgios Kokkonis: Department of Information and Electronic Systems Engineering, International Hellenic University, 57400 Thessaloniki, Greece
Vasileios Argyriou: Department of Networks and Digital Media, Kingston University, Kingston upon Thames KT1 2EE, UK
Thomas Lagkas: Department of Computer Science, International Hellenic University, 65404 Kavala, Greece
Sotirios K. Goudos: ELEDIA@AUTH, Physics Department, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Panagiotis Sarigiannidis: Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece

Agriculture, 2024, vol. 14, issue 2, 1-23

Abstract: Remote sensing stands out as one of the most widely used operations in the field. In this research area, UAVs offer full coverage of large cultivation areas in a few minutes and provide orthomosaic images with valuable information based on multispectral cameras. Especially for orchards, it is helpful to isolate each tree and then calculate the preferred vegetation indices separately. Thus, tree detection and crown extraction is another important research area in the domain of Smart Farming. In this paper, we propose an innovative tree detection method based on machine learning, designed to isolate each individual tree in an orchard. First, we evaluate the effectiveness of Detectron2 and YOLOv8 object detection algorithms in identifying individual trees and generating corresponding masks. Both algorithms yield satisfactory results in cherry tree detection, with the best F1-Score up to 94.85%. In the second stage, we apply a method based on OTSU thresholding to improve the provided masks and precisely cover the crowns of the detected trees. The proposed method achieves 85.30% on IoU while Detectron2 gives 79.83% and YOLOv8 has 75.36%. Our work uses cherry trees, but it is easy to apply to any other tree species. We believe that our approach will be a key factor in enabling health monitoring for each individual tree.

Keywords: machine learning; tree detection; remote sensing; smart farming; Detectron2; YOLOv8 (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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