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Automatic sex detection of individuals of Ceratitis capitata by means of computer vision in a biofactory

机译:通过计算机视觉在生物工厂中自动检测人头型角膜炎的性别

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BACKGROUND: The sterile insect technique (SIT) is acknowledged around the world as an effective method for biological pest control of Ceratitis capitata (Wiedemann). Sterile insects are produced in biofactories where one key issue is the selection of the progenitors that have to transmit specific genetic characteristics. Recombinant individuals must be removed as this colony is renewed. Nowadays, this task is performed manually, in a process that is extremely slow, painstaking and labour intensive, in which the sex of individuals must be identified. The paper explores the possibility of using vision sensors and pattern recognition algorithms for automated detection of recombinants. RESULTS: An automatic system is proposed and tested to inspect individual specimens of C. capitata using machine vision. It includes a backlighting system and image processing algorithms for determining the sex of live flies in five high-resolution images of each insect. The system is capable of identifying the sex of the flies by means of a program that analyses the contour of the abdomen, using fast Fourier transform features, to detect the presence of the ovipositor. Moreover, it can find the characteristic spatulate setae of males. Simulation tests with 1000 insects (5000 images) had 100% success in identifying male flies, with an error rate of 0.6% for female flies. CONCLUSION: This work establishes the basis for building a machine for the automatic detection and removal of recombinant individuals in the selection of progenitors for biofactories, which would have huge benefits for SIT around the globe.
机译:背景:无菌昆虫技术(SIT)在世界范围内被公认为是一种有效的控制人头角膜炎(Wiedemann)的生物害虫的方法。不育昆虫是在生物工厂中生产的,其中一个关键问题是选择必须具有特定遗传特征的祖细胞。随着该殖民地的更新,必须去除重组个体。如今,此任务是手动执行的,此过程非常缓慢,费力且劳动强度大,必须识别个体的性别。本文探讨了使用视觉传感器和模式识别算法自动检测重组体的可能性。结果:提出了一个自动系统,并进行了测试,以使用机器视觉检查人参的单个标本。它包括一个背光系统和图像处理算法,用于确定每种昆虫的五个高分辨率图像中的实蝇性别。该系统能够通过使用快速傅立叶变换特征分析腹部轮廓的程序来识别苍蝇的性别,以检测产卵器的存在。此外,它还能发现雄性的特征性扇形刚毛。用1000种昆虫(5000张图像)进行的模拟测试在识别雄蝇方面具有100%的成功率,雌蝇的错误率仅为0.6%。结论:这项工作为构建用于自动检测和去除重组个体选择生物工厂祖细胞的机器奠定了基础,这将为全球范围内的SIT带来巨大利益。

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