Integrated Scale-Adaptive Adjustment Factor-Enhanced BlendMask Method for Pineapple Processing System

Haotian Wang, Haojian Zhang (), Yukai Zhang, Jieren Deng, Chengbao Liu and Jie Tan
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Haotian Wang: School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
Haojian Zhang: Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Yukai Zhang: Institute of Physics, Chinese Academy of Sciences, Beijing 100080, China
Jieren Deng: Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Chengbao Liu: Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Jie Tan: Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

Agriculture, 2024, vol. 14, issue 9, 1-20

Abstract: This study addresses the challenge of efficiently peeling pineapples, which have a distinct elliptical form, thick skin, and small eyes that are difficult to detect with conventional automated methods. This results in significant flesh waste. To improve the process, we developed an integrated system combining an enhanced BlendMask method, termed SAAF-BlendMask, and a Pose Correction Planning (PCP) method. SAAF-BlendMask improves the detection of small pineapple eyes, while PCP ensures accurate posture adjustment for precise path planning. The system uses 3D vision and deep learning technologies, achieving an average precision (AP) of 73.04% and a small object precision (APs) of 62.54% in eye detection, with a path planning success rate reaching 99%. The fully automated electromechanical system was tested on 110 real pineapples, demonstrating a reduction in flesh waste by 11.7% compared to traditional methods. This study highlights the potential of advanced machine vision and robotics in enhancing the efficiency and precision of food processing.

Keywords: instance segmentation; 3D vision; automated processing; machine learning; path planning; mechatronics (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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