Assigning apples to individual trees in dense orchards using 3D colour point clouds

Zine-El-Abidine M., Dutağacı H., Galopin G., Rousseau D.

Biosystems Engineering, cilt.209, ss.30-52, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 209
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1016/j.biosystemseng.2021.06.015
  • Dergi Adı: Biosystems Engineering
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, Communication Abstracts, Compendex, Food Science & Technology Abstracts, INSPEC, Metadex, Veterinary Science Database, DIALNET, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.30-52
  • Anahtar Kelimeler: Fruit detection, Apple trees, Tree trunk detection, Point Cloud, Semantic segmentation, Phenotyping, FRUIT DETECTION, CITRUS-FRUIT, YIELD ESTIMATION, TRUNK-DETECTION, MACHINE VISION, GREEN APPLES, LOCALIZATION, IMAGES, RGB, RECOGNITION
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

© 2021 IAgrEWe propose a 3D colour point cloud processing pipeline to count apples on individual apple trees in trellis structured orchards. Fruit counting at the tree level requires separating trees, which is challenging in dense orchards. We employ point clouds acquired from the leaf-off orchard in winter period, where the branch structure is visible, to delineate tree crowns. We localise apples in point clouds acquired in harvest period. Alignment of the two point clouds enables mapping apple locations to the delineated winter cloud and assigning each apple to its bearing tree. Our apple assignment method achieves an accuracy rate higher than 95%. In addition to presenting a first proof of feasibility, we also provide suggestions for further improvement on our apple assignment pipeline.