Nondestructive perception of potato quality in actual online production based on cross-modal technology  

作　　者：Qiquan Wei Yurui Zheng Zhaoqing Chen Yun Huang Changqing Chen Zhenbo Wei Shuiqin Zhou Hongwei Sun Fengnong Chen 

机构地区：[1]School of Automation,Hangzhou Dianzi University,Hangzhou 310018,China [2]School of Information Engineering and Internet of Things,Huzhou Vocational and Technical College,Huzhou 313000,Zhejiang,China [3]Jinhua Academy of Agricultural Sciences,Jinhua 321017,Zhejiang,China [4]College of Biosystems Engineering and Food Science,Zhejiang University,Hangzhou 310058,China [5]Fair Friend Institute of Intelligent Manufacturing,Hangzhou Vocational and Technical College,Hangzhou 310018,China

出　　处：《International Journal of Agricultural and Biological Engineering》2023年第6期280-290,共11页国际农业与生物工程学报（英文）

基　　金：supported by the Zhejiang Province Key Research and Development Program(Grant No.2021C02011);Zhejiang Province Public Welfare Technology Application Research Project(Grant No.LGN18-F030002);Hangzhou Science and Technology Bureau(Grant No.20201203B116);Program of“Xinmiao”(Potential)Talents in Zhejiang Province(Grant Number:2022R4-07B055);the Graduate Scientific Research Foundation of Hangzhou Dianzi University(Grant No.CXJJ2022177);the Major Science and Technology Projects of Breeding New Varieties of Agriculture in Zhejiang Province(Grant No.2021C02074).

摘　　要：Nowadays,China stands as the global leader in terms of potato planting area and total potato production.The rapid and nondestructive detection of the potato quality before processing is of great significance in promoting rural revitalization and augmenting farmers’income.However,existing potato quality sorting methods are primarily confined to theoretical research,and the market lacks an integrated intelligent detection system.Therefore,there is an urgent need for a post-harvest potato detection method adapted to the actual production needs.The study proposes a potato quality sorting method based on cross-modal technology.First,an industrial camera obtains image information for external quality detection.A model using the YOLOv5s algorithm to detect external green-skinned,germinated,rot and mechanical damage defects.VIS/NIR spectroscopy is used to obtain spectral information for internal quality detection.A convolutional neural network(CNN)algorithm is used to detect internal blackheart disease defects.The mean average precision(mAP)of the external detection model is 0.892 when intersection of union(IoU)=0.5.The accuracy of the internal detection model is 98.2%.The real-time dynamic defect detection rate for the final online detection system is 91.3%,and the average detection time is 350 ms per potato.In contrast to samples collected in an ideal laboratory setting for analysis,the dynamic detection results of this study are more applicable based on a real-time online working environment.It also provides a valuable reference for the subsequent online quality testing of similar agricultural products.

关 键 词：cross-modal technology potato quality YOLOv5s VIS/NIR spectroscopy online nondestructive detection 

分 类 号：S22[农业科学—农业机械化工程]