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Non-Contact Detection of Wine Grape Load Volume in Hopper During Mechanical Harvesting

文献类型： 外文期刊

作者： Liu, Haowei 1 ; Wang, Xiu 2 ; Song, Jian 2 ; Chen, Mingzhou 2 ; Li, Cuiling 2 ; Zhai, Changyuan 1 ;

作者机构： 1.Xinjiang Agr Univ, Coll Mech & Elect Engn, Urumqi 830052, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing 100097, Peoples R China

3.Natl Engn Res Ctr Intelligent Equipment Agr NERCIE, Beijing 100097, Peoples R China

4.Beijing PAIDE Sci & Technol Dev Co Ltd, Beijing 100097, Peoples R China

关键词： wine grapes; ultrasonic sensors; load detection; regression model

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.8 ）

ISSN：

年卷期： 2025 年 15 卷 9 期

页码：

收录情况： SCI

摘要： Issues of poor real-time performance and low accuracy in the detection of load volume in the hopper during the mechanized harvesting of wine grapes are addressed in this study through the development of a proposed volume detection method based on ultrasonic sensors. First, the ultrasonic sensor beamwidth and detection height were determined through calibration tests. Next, a test bench was used to explore the influence of the number of ultrasonic sensors and conveying speed on the detected grape pile height. Data-based regression and hopper configuration-based geometric models correlating grape load volume with detected pile height were subsequently constructed; their accuracies were compared using test bench experiments to identify the optimal detection scheme. The regression model was more accurate than the geometric model under the considered conveying speeds with a maximum relative error of 8.0% for the former. Finally, field tests determined that the average grape load volume detection error during actual harvesting was 14.4%. Therefore, this study provides an effective solution for the detection of grape load volume in the hopper during mechanized harvesting and establishes a theoretical basis for the development of intelligent grape harvesting methods.

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