A Polygonatum sibiricum Seed Maturity Recognition Model Based on Improved Convolutional Neural Network YOLOv8n
Feng Feng
Kong Chun
Chen Shiyao
Ma Chunyue
Wang Chen
Guo Xiuru
Sun Bo
Wang Zhijun
Abstract：Polygonatum sibiricum is an important medicinal and edible homologous variety.In the process of planting and breeding of P.sibiricum,manual methods based on personal experience are usually used to iden-tify the maturity of P.sibiricum seeds.But this method has problems of strong subjectivity and unstable accuracy,which will affect the yield and quality of subsequent P.sibiricum planting to a certain extent,and thus affect the product quality and economic benefits of the downstream industry of P.sibiricum.On the other hand,due to the scattered planting areas of P.sibiricum,the method of manually identifying maturity was relatively inefficient.To address the above issues,this paper proposed a maturity recognition model for P.sibiricum seeds based on an improved convolutional neural network YOLOv8n that was YOLOv8n-FasterNet-EMA.Firstly,in terms of model lightweighting,a new c2f-FasterNet module was obtained by replacing the original convolution in the YOLOv8n backbone network with PConv convolution structure of FasterNet which was then connected to the Bottleneck lay-er,thereby reduced the computational complexity and memory usage of the model.Then,in terms of model gen-eralization performance,the feature extraction ability and model generalization ability of the model were im-proved by combining the EMA attention mechanism with the c2f module at the detection point of the YOLOv8n neck network.To verify the performance of the proposed model,comparative experiments were conducted on the collected P.sibiricum seed maturity datasets.The experiments showed that compared with the original YOLOv8n model,the proposed model improved the average recognition accuracy by 2.1％,while reduced the number of model parameters by 21.3％.In addition,compared with SSD and YOLOv5n,the model proposed in this study achieved certain improvements in both recognition accuracy and speed.Therefore,the proposed model could ef-fectively identify the maturity of P.sibiricum seeds,which is of great practical significance for achieving the in-telligent recognition of P.sibiricum seed maturity,and further improving the quality of P.sibiricum breeding,as well as improving the economic benefits of downstream industries.
Keywords：Polygonatum sibiricumSeed maturity recognitionConvolutional neural network YOLOv8nFasterNetEMA attention mechanism
Publication Date：2025-01-27
Online Publishing Date：2026-05-22(First online date of this platform, not the publication date of the document)
Pages：10( 156-165 )