A Lightweight Deep Learning Semantic Segmentation Model for Optical-Image-Based Post-Harvest Fruit Ripeness Analysis of Sugar Apples ( Annona squamosa )

Zewen Xie, Zhenyu Ke, Kuigeng Chen, Yinglin Wang, Yadong Tang and Wenlong Wang ()
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Zewen Xie: School of Physics and Materials Science, Guangzhou University, Guangzhou 510006, China
Zhenyu Ke: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Kuigeng Chen: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Yinglin Wang: School of Life Sciences, Guangzhou University, Guangzhou 510006, China
Yadong Tang: School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou 510006, China
Wenlong Wang: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China

Agriculture, 2024, vol. 14, issue 4, 1-22

Abstract: The sugar apple ( Annona squamosa ) is valued for its taste, nutritional richness, and versatility, making it suitable for fresh consumption and medicinal use with significant commercial potential. Widely found in the tropical Americas and Asia’s tropical or subtropical regions, it faces challenges in post-harvest ripeness assessment, predominantly reliant on manual inspection, leading to inefficiency and high labor costs. This paper explores the application of computer vision techniques in detecting ripeness levels of harvested sugar apples and proposes an improved deep learning model (ECD-DeepLabv3+) specifically designed for ripeness detection tasks. Firstly, the proposed model adopts a lightweight backbone (MobileNetV2), reducing complexity while maintaining performance through MobileNetV2′s unique design. Secondly, it incorporates the efficient channel attention (ECA) module to enhance focus on the input image and capture crucial feature information. Additionally, a Dense ASPP module is introduced, which enhances the model’s perceptual ability and expands the receptive field by stacking feature maps processed with different dilation rates. Lastly, the proposed model emphasizes the spatial information of sugar apples at different ripeness levels by the coordinate attention (CA) module. Model performance is validated using a self-made dataset of harvested optical images categorized into three ripeness levels. The proposed model (ECD-DeepLabv3+) achieves values of 89.95% for MIoU , 94.58% for MPA , 96.60% for PA , and 94.61% for MF1 , respectively. Compared to the original DeepLabv3+, it greatly reduces the number of model parameters ( Params ) and floating-point operations ( Flops ) by 89.20% and 69.09%, respectively. Moreover, the proposed method could be directly applied to optical images obtained from the surface of the sugar apple, which provides a potential solution for the detection of post-harvest fruit ripeness.

Keywords: deep learning; computer vision; post-harvest fruit ripeness; DeepLabv3+; sugar apple ( Annona squamosa ); optical image (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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