Automated screening of canola seeds using GPU-based deep learning: A functional prototype

这个Engage Plus赠款给予20/20种子实验室，一家阿尔伯塔省的公司，访问独特的知识和专业知识，可在Dr. Dileepan约瑟夫的实验室在阿尔伯塔大学。目的是解决一个公司特有的问题，即使用光学显微镜对油菜籽进行自动筛选。油菜籽是20世纪70年代从油菜籽中培育出来的，此后不断改良，是加拿大的一项具有全球重要性的创新。据加拿大油菜籽委员会称，仅在加拿大，油菜籽产业“每年创造193亿美元的经济活动和近25万个就业岗位”。此外，“菜籽油有科学证据证明其对[关键]健康问题的益处”，菜籽油是一种极好的牲畜饲料，有助于满足世界对蛋白质日益增长的需求。为了满足贸易要求，种子纯度一直是一个值得关注的问题。加拿大食品检验局在种子方面拥有世界上最高的标准，20/20种子实验室是加拿大第一个获得国际种子检测协会认证的私人实验室。自动化纯度分析的可视化方面，例如：在美国，识别不需要的类似油菜籽的种子是自动清洁的先决条件，目前，颜色分类只能部分完成这项工作。在之前的Engage Grant项目中，这个产学研合作项目成功展示了利用多层卷积神经网络（也称为深度学习）从六种不需要的菜籽油中区分菜籽油的可行性。正如预期的那样，并行处理被证明是重要的，经过一些调查，图形处理单元，即gpu，似乎是最合适的。此Engage Plus补助金的目的是使上述概念证明在功能上对自动化纯度分析有用。除了改进由Engage Grant提供的名为PurityAnalyzer 1.0的软件外，还将设计、实施和测试一个包含软件和定制硬件的原型，以实时执行油菜籽的自动筛选。

This Engage Plus Grant gives 20/20 Seed Labs, an Albertan company, access to unique knowledge andexpertise available in Dr. Dileepan Joseph's lab at the University of Alberta. The purpose is to address acompany-specific problem, namely the automated screening of canola seeds using optical microscopy.Canola, bred from rapeseed in the 1970s and continuously improved thereafter, is a Canadian innovation ofworldwide importance. In Canada alone, the canola industry "generates $19.3 billion annually in economicactivity and nearly a quarter of a million jobs," according to the Canola Council of Canada. Moreover, "canolaoil has the science to prove its benefits on [critical] health issues," and canola meal is an excellent livestockfeed to help meet the world's growing demand for protein.To meet trade requirements, seed purity is always a concern. The Canadian Food Inspection Agency hassome of the highest standards in the world regarding seed and 20/20 Seed Labs is the first private lab in Canadato be accredited by the International Seed Testing Association. Automating visual aspects of purity analysis,i.e., to identify unwanted seeds that resemble canola, is a prerequisite for automated cleaning, something thatcurrently is only partially accomplished by colour sorting.In a previous Engage Grant, this industry-university partnership successfully demonstrated the feasibility ofdiscriminating canola from six unwanted species using multilayer convolutional neural networks, an approachalso known as deep learning. As expected, parallel processing proved important and, after some investigation,graphics processing units, i.e., GPUs, appeared to be most suitable.The objective of this Engage Plus Grant is to make the above proof of concept functionally useful forautomated purity analysis. In addition to improving software delivered by the Engage Grant, entitled PurityAnalyzer 1.0, a prototype comprising both software and custom hardware will be designed, implemented, andtested to perform automated screening of canola seeds in real time.