农业机械学报2025，Vol.56Issue(1)：74-83,10.DOI:10.6041/j.issn.1000-1298.2025.01.008
基于视觉触觉双重迁移学习的番茄成熟度检测方法
Tomato Maturity Detection Method Based on Visual and Haptic Double Transfer Learning
摘要
Abstract
Aiming at the problem that tomato ripeness cannot be accurately recognized by relying only on visual technology in the current automated picking process,a tomato ripeness detection method based on visual-haptic dual migration learning was proposed.The method firstly adopted the visual and haptic double transfer learning fusion algorithm as the feature extraction fusion module to solve the problem of not effectively extracting tomato feature information.Secondly,the soft parameter sharing-multilabel classification method was used as the classification module to avoid overfitting by increasing the correlation between different classification tasks.Focusing on tomato varieties that ripened to a single color,such as red and yellow fruits,experimental studies on a newly developed visual and tactile dataset were conducted.The experiments showed that the parameter count of the soft parameter sharing-multilabel detection model was 1.882 × 107,and the ripeness AUC score reached 0.977 3.Compared with the detection models of uncertainty weighted loss,adaptive hard parameter sharing,cross-stitch network,and soft parameter sharing,the parameter counts dropped by 3.08 × 106,6.16 × 106,3.08 × 106,and 3.08 × 106,and the ripeness AUC scores increased by 0.017 5,0.017 9,0.026 7 and 0.008 9,respectively.This indicated that the method improved the detection of tomato ripeness during automated picking to a certain extent and provided an effective solution to the tomato ripeness detection problem.关键词
番茄成熟度/机器视觉/机器触觉/双重迁移学习/软参数共享-多标签Key words
tomato maturity/machine vision/machine haptics/double transfer learning/soft parameter sharing-multilabel分类
计算机与自动化引用本文复制引用
张鹏,杜东峰,李爽,单东日,陈振学..基于视觉触觉双重迁移学习的番茄成熟度检测方法[J].农业机械学报,2025,56(1):74-83,10.基金项目
山东省重大科技创新工程项目(2019JZZY010444)和齐鲁工业大学(山东省科学院)科教产融合培优基金项目(2023PY006) （2019JZZY010444）
