Using remote sensing to identify individual tree species in orchards: A review


Scientia Horticulturae, vol.321, 2023 (SCI-Expanded) identifier

  • Publication Type: Article / Review
  • Volume: 321
  • Publication Date: 2023
  • Doi Number: 10.1016/j.scienta.2023.112333
  • Journal Name: Scientia Horticulturae
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, CAB Abstracts, Environment Index, Food Science & Technology Abstracts, Veterinary Science Database
  • Keywords: Climatic zones, Crown delineation, Deep learning, Fruit trees, Image analysis, Machine learning, Orchards, Remote sensing, Tree identification
  • Hacettepe University Affiliated: Yes


Fruit trees are an essential subset of all tree species due to their high water and nutrient content. They play a vital role in human nutrition and provide a significant economic boost for top pomiculture countries. The purpose of this article was to investigate the published articles based on the categorization of orchard trees in accordance with the various climatic zones and conduct a review related to the methods for the identification of individual fruit trees in orchards. The review looked into the methods that have been used in the past to identify orchard trees and define the crowns of those trees. We highlight 74 articles that were published in 22 different journals published in the Web of Science database. A wide variety of conventional and modern digital image analysis techniques, including deep learning techniques, can be used to facilitate the efficient utilization of products derived from space-borne, airborne, and terrestrial systems. We believe that efficient orchard management to support consistent and sufficient fruit yields is a goal that should be prioritized. In this respect, fruit tree identification and modeling procedures are continually being enhanced and expanded thanks to ongoing research and development efforts. In this context, this review provides a detailed overview of the key aspects of the major efforts proposed for identifying individual fruit trees in orchards.