We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Classification of Fluorescently Labelled Maize Kernels Using Convolutional Neural Networks.
- Authors
Wang, Zilong; Guan, Ben; Tang, Wenbo; Wu, Suowei; Ma, Xuejie; Niu, Hao; Wan, Xiangyuan; Zang, Yong
- 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.
- Subjects
CONVOLUTIONAL neural networks; COMPUTER vision; CORN seeds; FLUORESCENT proteins; LIGHT sources; LIGHT filters; CORN
- Publication
Sensors (14248220), 2023, Vol 23, Issue 5, p2840
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s23052840