Design and feasibility analysis of a graded harvesting end-effector with the function of soluble solid content estimation  

作　　者：Yufei Lin Hao Liang Junhua Tong Haoyu Shen Xiaping Fu Chuanyu Wu 

机构地区：[1]College of Mechanical Engineering,Zhejiang Sci-Tech University,Hangzhou 310018,China [2]Department of Agriculture and Biotechnology,Wenzhou Vocational College of Science and Technology,Wenzhou 325006,Zhejiang,China [3]College of Mathematics and Computer Science,Zhejiang A&F University,Hangzhou 311300,China [4]Zhejiang Ocean University,Zhoushan 316002,Zhejiang,China

出　　处：《International Journal of Agricultural and Biological Engineering》2024年第5期239-246,共8页国际农业与生物工程学报（英文）

基　　金：supported by the National Natural Science Foundation of China (Grant No. U23A20175);the “Leading Goose” R&D Program of Zhejiang (Grant No. 2022C02052);the Scientific Research Fund of Zhejiang Provincial Education Department (Grant No. Y202250747);Wenzhou Science and Technology Commissioner Special Project (Grant No. X2023045).

摘　　要：In response to the prevailing scarcity of labor and with the aim of augmenting the proportion of premium-quality fruits, a robotic grading end-effector system for harvesting was meticulously designed. The end-effector could measure the soluble solid content (SSC) of peaches during the harvesting process to evaluate the quality of the fruit, thereby facilitating real-time grading during harvesting. As comprising a harvesting component and an information-gathering segment, the end-effector system was optimized with the primary structural parameters of its adaptive fingers using a mathematical model of peach morphology. Also, the buffering materials for mitigating the pressure exerted by the adaptive fingers on the peaches were compared. Furthermore, feasibility analyses of the grasping actions were conducted based on the interaction forces between the adaptive fingers and the peaches. To grade the quality of peaches, SSC was used as an indicator to assess and grade the quality of the peaches. The spectra of peaches within the wavelength range of 590-1100 nm were collected, and a predictive model for SSC was developed. The correlation coefficients for the calibration set and prediction sets of the predictive model were 0.880 and 0.890, with corresponding root mean square errors of 0.370% and 0.357% Brix, respectively. In addition, a robustness and accuracy assessment was conducted using 30 peach samples, yielding a correlation coefficient of 0.936 and a standard error of 0.386% Brix between the predicted and measured values of SSC. The results confirm that the end-effector can measure the SSC of peaches during the collection process, providing novel concepts and theoretical foundations for real-time harvesting and grading.

关 键 词：robot harvesting graded end-effector soluble solids near-infrared spectroscopy 

分 类 号：S22[农业科学—农业机械化工程]