Deep Learning-Based Segmentation of Intertwined Fruit Trees for Agricultural Tasks

Young-Jae La, Dasom Seo, Junhyeok Kang, Minwoo Kim, Tae-Woong Yoo and Il-Seok Oh ()
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Young-Jae La: Department of Computer Science and Artificial Intelligence/CAIIT, Jeonbuk National University, Jeonju 54896, Republic of Korea
Dasom Seo: Department of Computer Science and Artificial Intelligence/CAIIT, Jeonbuk National University, Jeonju 54896, Republic of Korea
Junhyeok Kang: Department of Computer Science and Artificial Intelligence/CAIIT, Jeonbuk National University, Jeonju 54896, Republic of Korea
Minwoo Kim: Department of Computer Science and Artificial Intelligence/CAIIT, Jeonbuk National University, Jeonju 54896, Republic of Korea
Tae-Woong Yoo: Department of Computer Science and Artificial Intelligence/CAIIT, Jeonbuk National University, Jeonju 54896, Republic of Korea
Il-Seok Oh: Department of Computer Science and Artificial Intelligence/CAIIT, Jeonbuk National University, Jeonju 54896, Republic of Korea

Agriculture, 2023, vol. 13, issue 11, 1-19

Abstract: Fruit trees in orchards are typically placed at equal distances in rows; therefore, their branches are intertwined. The precise segmentation of a target tree in this situation is very important for many agricultural tasks, such as yield estimation, phenotyping, spraying, and pruning. However, our survey on tree segmentation revealed that no study has explicitly addressed this intertwining situation. This paper presents a novel dataset in which a precise tree region is labeled carefully by a human annotator by delineating the branches and trunk of a target apple tree. Because traditional rule-based image segmentation methods neglect semantic considerations, we employed cutting-edge deep learning models. Five recently pre-trained deep learning models for segmentation were modified to suit tree segmentation and were fine-tuned using our dataset. The experimental results show that YOLOv8 produces the best average precision (AP), 93.7 box AP@0.5:0.95 and 84.2 mask AP@0.5:0.95. We believe that our model can be successfully applied to various agricultural tasks.

Keywords: tree segmentation; deep learning; branch intertwining; fine-tuning; apple tree; agricultural automation (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: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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