Affiliation:
1. College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071001, China
2. National Research Facility for Phenotypic and Genotypic Analysis of Model Animals (Beijing), China Agricultural University, Beijing 100193, China
Abstract
To address the current issues of low intelligence and accuracy in seed-sorting devices, an intelligent seed sorter was developed in this study using machine-vision technology and the lightweight YOLOv5n. The machine consisted of a transmission system, feeding system, image acquisition system, and seed screening system. A lightweight YOLOv5n model, FS-YOLOv5n, was trained using 4756 images, incorporating FasterNet, Local Convolution (PConv), and a squeeze-and-excitation (SE) attention mechanism to improve feature extraction efficiency, detection accuracy, and reduce redundancy. Taking ‘Zhengdan 958’ corn seeds as the research object, a quality identification and seed sorting test was conducted on six test groups (each consisting of 1000 seeds) using the FS-YOLOv5n model. Following lightweight improvements, the machine showed an 81% reduction in parameters and floating-point operations compared to baseline models. The intelligent seed sorter achieved an average sorting rate of 90.76%, effectively satisfying the seed-sorting requirements.
Funder
earmarked fund for CARS
Hebei province innovation research group project
Baoding city science and technology plan
Reference21 articles.
1. Varietal classification of maize seeds using computer vision and machine learning techniques;Xu;J. Food Process Eng.,2021
2. Online monitoring method of mechanized soybean harvest quality based on machine vision;Chen;Trans. Chin. Soc. Agric. Mach.,2021
3. Real−time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks;Jiang;IEEE Access,2019
4. Vis-NIR hyperspectral imaging combined with incremental learning for open world maize seed varieties identification;Zhang;Comput. Electron. Agric.,2022
5. Bi, C.G., Hu, N., Zou, Y.Q., Zhang, S., Xu, S.Z., and Yu, H.L. (2022). Development of deep learning methodology for maize seed variety recognition based on improved Swin transformer. Agronomy, 12.