{"title":"Quality Classification and Segmentation of Sugarcane Billets Using Machine Vision","authors":"","doi":"10.1109/DICTA56598.2022.10034561","DOIUrl":null,"url":null,"abstract":"Machine learning is widely used in agriculture to optimize practices such as planting, crop detection, and harvesting. The sugar industry is a major contributor to the global economy, valuable both as a food source and as a sustainable crop with useful byproducts. This paper presents three machine vision algorithms capable of performing quality classification and segmentation of raw sugarcane billets, developing a proof-of-concept for implementation at our industry partner's mill in NSW. Such a system has the potential to improve quality and reduce costs associated with an essential yet labor-intensive, inefficient, and unreliable process. Two recent iterations of the popular You Only Look Once (YOLO) algorithm, YOLOR and YOLOX, are trained for classification, with the state-of-the-art Mask R-CNN network used for segmentation. The best performing classification model, YOLOX, achieves a classification mAP50:95 of 90.1% across 7 classes in real time, with an average inference speed of 19.36 ms per image. Segmentation accuracy of AP50 of 70.8% and AR50-95 of 83.5% was achieved using the Mask CNN-R network.","PeriodicalId":159377,"journal":{"name":"2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DICTA56598.2022.10034561","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Machine learning is widely used in agriculture to optimize practices such as planting, crop detection, and harvesting. The sugar industry is a major contributor to the global economy, valuable both as a food source and as a sustainable crop with useful byproducts. This paper presents three machine vision algorithms capable of performing quality classification and segmentation of raw sugarcane billets, developing a proof-of-concept for implementation at our industry partner's mill in NSW. Such a system has the potential to improve quality and reduce costs associated with an essential yet labor-intensive, inefficient, and unreliable process. Two recent iterations of the popular You Only Look Once (YOLO) algorithm, YOLOR and YOLOX, are trained for classification, with the state-of-the-art Mask R-CNN network used for segmentation. The best performing classification model, YOLOX, achieves a classification mAP50:95 of 90.1% across 7 classes in real time, with an average inference speed of 19.36 ms per image. Segmentation accuracy of AP50 of 70.8% and AR50-95 of 83.5% was achieved using the Mask CNN-R network.