Instance Segmentation of Sugar Apple ( Annona squamosa ) in Natural Orchard Scenes Using an Improved YOLOv9-seg Model

Guanquan Zhu, Zihang Luo, Minyi Ye, Zewen Xie, Xiaolin Luo, Hanhong Hu, Yinglin Wang, Zhenyu Ke, Jiaguo Jiang and Wenlong Wang ()
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Guanquan Zhu: School of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, China
Zihang Luo: School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China
Minyi Ye: School of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, China
Zewen Xie: School of Physics and Materials Science, Guangzhou University, Guangzhou 510006, China
Xiaolin Luo: School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China
Hanhong Hu: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Yinglin Wang: School of Life Sciences, South China Normal University, Guangzhou 510631, China
Zhenyu Ke: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Jiaguo Jiang: School of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, China
Wenlong Wang: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China

Agriculture, 2025, vol. 15, issue 12, 1-30

Abstract: Sugar apple ( Annona squamosa ) is prized for its excellent taste, rich nutrition, and diverse uses, making it valuable for both fresh consumption and medicinal purposes. Predominantly found in tropical regions of the Americas and Asia, its harvesting remains labor-intensive in orchard settings, resulting in low efficiency and high costs. This study investigates the use of computer vision for sugar apple instance segmentation and introduces an improved deep learning model, GCE-YOLOv9-seg, specifically designed for orchard conditions. The model incorporates Gamma Correction (GC) to enhance image brightness and contrast, improving target region identification and feature extraction in orchard settings. An Efficient Multiscale Attention (EMA) mechanism was added to strengthen feature representation across scales, addressing sugar apple variability and maturity differences. Additionally, a Convolutional Block Attention Module (CBAM) refined the focus on key regions and deep semantic features. The model’s performance was evaluated on a self-constructed dataset of sugar apple instance segmentation images captured under natural orchard conditions. The experimental results demonstrate that the proposed GCE-YOLOv9-seg model achieved an F1 score ( F1 ) of 90.0%, a precision ( P ) of 89.6%, a recall ( R ) level of 93.4%, a mAP@0.5 of 73.2%, and a mAP@[0.5:0.95] of 73.2%. Compared to the original YOLOv9-seg model, the proposed GCE-YOLOv9-seg showed improvements of 1.5% in the F1 score and 3.0% in recall for object detection, while the segmentation task exhibited increases of 0.3% in mAP@0.5 and 1.0% in mAP@[0.5:0.95]. Furthermore, when compared to the latest model YOLOv12-seg, the proposed GCE-YOLOv9-seg still outperformed with an F1 score increase of 2.8%, a precision (P) improvement of 0.4%, and a substantial recall (R) boost of 5.0%. In the segmentation task, mAP@0.5 rose by 3.8%, while mAP@[0.5:0.95] demonstrated a significant enhancement of 7.9%. This method may be directly applied to sugar apple instance segmentation, providing a promising solution for automated sugar apple detection in natural orchard environments.

Keywords: deep learning; computer vision; YOLOv9-seg; sugar apple ( Annona squamosa ) (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: 2025
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