Quality Classification and Segmentation of Sugarcane Billets Using Machine Vision

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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.
基于机器视觉的甘蔗坯质量分类与分割
机器学习广泛应用于农业,用于优化种植、作物检测和收获等实践。制糖业是全球经济的主要贡献者,作为食物来源和具有有用副产品的可持续作物都很有价值。本文介绍了三种机器视觉算法,能够对原甘蔗坯料进行质量分类和分割,并在新南威尔士州的行业合作伙伴的工厂中开发了概念验证。这样的系统具有提高质量和降低成本的潜力,这些成本与一个必要但劳力密集、效率低下和不可靠的过程有关。流行的YOLO (You Only Look Once)算法的两个最新迭代,即YOLO和YOLOX,用于分类训练,最先进的Mask R-CNN网络用于分割。表现最好的分类模型YOLOX在7个类别中实现了90.1%的实时分类mAP50:95,平均每张图像的推理速度为19.36 ms。利用Mask CNN-R网络实现了AP50和AR50-95的分割准确率分别为70.8%和83.5%。
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