内蒙古农业大学学报（自然科学版）2026，Vol.47Issue(3)：67-75,9.DOI:10.16853/j.cnki.1009-3575.2026.03.009
基于小波重构与FastICA的巨菌草种茎节点快速检测算法研究
Fast Detection Algorithm for Pennisetum giganteum Seed Stem Node Based on Wavelet Reconstruction and FastICA
摘要
Abstract
To meet the node positioning requirements in the automated production of Pennisetum giganteum seedlings,this paper pro-posed a detection algorithm based on the wavelet multi-scale denoising and fast independent component analysis(FastICA)method.The contour signal of seed stem was obtained by a laser displacement sensor,and the db5 wavelet base was used for eight-layer dis-crete wavelet decomposition.The fifth to seventh layer coefficients were selected for multi-threshold processing and reconstruction to suppress noise interference.By combining this with the time delay embedding technology,a multidimensional observation matrix was constructed,and the FastICA was used to realize blind source separation.Fuzzy constraints were applied to node localization based on the biological characteristics of internodes.The experiment involved 100 Pennisetum giganteum seed stems,totaling 600 nodes with buds,for verification.The results showed that the node positioning accuracy was 100%,the maximum error was 1.10 mm,and the average absolute error was 0.42 mm.When the sensor moved and collected data at a speed of 63 cm/s,the average detection time was 0.19 seconds,and both the accuracy and speed met the requirements of the production line.The algorithm provided an ef-fective technical scheme for automatic seed production of Pennisetum giganteum seedlings.关键词
巨菌草/节点检测/小波分析/快速独立成分分析/盲源分离Key words
Pennisetum giganteum/Node positioning/Wavelet analysis/Rapid independent component analysis/Blind source separation分类
农业科技引用本文复制引用
李紫航,郁志宏,张建超,马学杰,苏强,刘文航..基于小波重构与FastICA的巨菌草种茎节点快速检测算法研究[J].内蒙古农业大学学报（自然科学版）,2026,47(3):67-75,9.基金项目
内蒙古自治区自然科学基金项目(2025LHMS03020) （2025LHMS03020）
