Study on Predicting Blueberry Hardness from Images for Adjusting Mechanical Gripper Force

Hao Yin, Wenxin Li, Han Wang, Yuhuan Li, Jiang Liu and Baogang Li ()
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Hao Yin: School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China
Wenxin Li: School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China
Han Wang: College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan 030024, China
Yuhuan Li: School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China
Jiang Liu: School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China
Baogang Li: School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China

Agriculture, 2025, vol. 15, issue 6, 1-23

Abstract: Precision and non-damaging harvesting is a key direction for the development of mechanized fruit harvesting technologies. Blueberries, with their soft texture and delicate skin, present significant challenges for achieving precise and non-damaging mechanical harvesting. This paper proposes an intelligent recognition and prediction method based on machine vision. The method uses image recognition technology to extract the physical characteristics of blueberries, such as diameter and thickness, and estimates fruit hardness in real-time through a predictive model. The gripping force of the mechanical claw is dynamically adjusted to ensure non-destructive harvesting. Firstly, a chimpanzee optimization algorithm (ChOA) was used to optimize a prediction model that established a mapping relationship between fruit diameter, thickness, weight, and fruit hardness. The radial basis network optimized by the chimpanzee optimization algorithm (ChOA-RBF) model was compared with a non-optimized model, and the results showed that the ChOA-RBF prediction model has significant advantages in predicting fruit hardness. Next, an orthogonal experiment further verified the model, showing that the prediction error between the model’s values and actual values was less than 5%. Additionally, considering practical applications, a simple and efficient two-parameter method was proposed, removing the weight parameter and predicting fruit hardness using only diameter and thickness. Although the two-parameter method increases the prediction error by 0.36% compared to the three-parameter method, it reduces the number of convergence steps by 71 and shortens the computation time by one-third, significantly improving iteration speed. Finally, further crushing experiments showed that using the two-parameter method for hardness prediction through parameter extraction via visual recognition resulted in a relative error of less than 8%, with an average relative error of 3.91%. The error falls within the acceptable range for the safety factor design. This method provides a novel solution for the non-damaging mechanized harvesting of soft fruits.

Keywords: fruit hardness; non-destructive harvesting; machine vision; predictive modeling; ChOA-RBF (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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