Non-destructive and contactless quality evaluation of table grapes by a computer vision system

作     者：Cavallo, Dario Pietro Cefola, Maria Pace, Bernardo Logrieco, Antonio Francesco Attolico, Giovanni 

作者机构：CNR Natl Res Council Italy Inst Intelligent Syst Automat Via G Amendola 122-O I-70126 Bari Italy CNR Natl Res Council Italy Inst Sci Food Prod Via G Amendola 122-O I-70126 Bari Italy CNR Natl Res Council Italy Inst Sci Food Prod URT CS DAT Via Michele Protano I-71121 Foggia Italy CNR Natl Res Council Italy Inst Intelligent Ind Syst & Technol Adv Mfg Via G Amendola 122-O I-70126 Bari Italy 

出 版 物：《COMPUTERS AND ELECTRONICS IN AGRICULTURE》 (农用计算机与电子设备)

年 卷 期：2019年第156卷

页      面：558-564页

核心收录：

学科分类：09[农学] 0901[农学-作物学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Puglia Region 'Avviso Aiuti a Sostegno dei Cluster Tecnologici Regionali per l'Innovazione' [VFQA3D0] 

主　　题：Table grapes Quality evaluation Computer vision system Random forest classifier 

摘      要：Quality rating is currently accomplished by non-destructive and subjective sensory evaluation or by objective and destructive analytical techniques. There is a strong need of an objective non-destructive contactless quality evaluation system to monitor fruit and vegetable along the whole supply chain. This paper proposes a Computer vision system to satisfy this request. Image processing and machine learning techniques have been combined to develop a Computer vision system whose configuration and tuning has been strongly simplified: that makes easier its deployment in real applications. The system has been verified on two white table grape cultivars (Italia and Victoria) against three different classification tasks. The first considered five quality levels (5, 4, 3, 2, 1);the second separated the higher fully marketable quality levels (5 and 4) from the boundary (3) and the waste (2 and 1);the third separated the higher fully marketable quality levels (5 and 4) from the other three (3, 2 and 1). The system achieved a cross-validation classification accuracy up to 92% on the cultivar Victoria and up to 100% on the cultivar Italia for binary or binomial classification between fully marketable and residual quality levels. The obtained results support its capability of powerfully, flexibly and continuously monitoring the quality of the complete production along the whole supply chain.