Classification of Fluorescently Labelled Maize Kernels Using Convolutional Neural Networks.

Abstract

Accurate real-time classification of fluorescently labelled maize kernels is important for the industrial application of its advanced breeding techniques. Therefore, it is necessary to develop a real-time classification device and recognition algorithm for fluorescently labelled maize kernels. In this study, a machine vision (MV) system capable of identifying fluorescent maize kernels in real time was designed using a fluorescent protein excitation light source and a filter to achieve optimal detection. A high-precision method for identifying fluorescent maize kernels based on a YOLOv5s convolutional neural network (CNN) was developed. The kernel sorting effects of the improved YOLOv5s model, as well as other YOLO models, were analysed and compared. The results show that using a yellow LED light as an excitation light source combined with an industrial camera filter with a central wavelength of 645 nm achieves the best recognition effect for fluorescent maize kernels. Using the improved YOLOv5s algorithm can increase the recognition accuracy of fluorescent maize kernels to 96%. This study provides a feasible technical solution for the high-precision, real-time classification of fluorescent maize kernels and has universal technical value for the efficient identification and classification of various fluorescently labelled plant seeds.

Plant phenotyping relevance

蛍光標識されたトウモロコシ種子という植物の状態を、機械視覚とCNNでリアルタイム識別する取得・解析手法が研究の中心であり、単なる生物実験の routine 測定ではない。

Code and data availability

The supplied blocks describe a fluorescent maize kernel dataset (5000 images, 3000 kernel samples) and an improved YOLOv5s model, but contain no data or code availability statement, no public repository deposit, and no authors' URL for the dataset, images, annotations, or trained model. Only generic software (LabelImg,

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