农机化研究2025，Vol.47Issue(8)：243-250,8.DOI:10.13427/j.issn.1003-188X.2025.08.034
基于光谱技术的穴盘苗品质分类算法研究
Quality Classification Algorithm of Plug Seedling Based on Spectral Technology
摘要
Abstract
It is inevitable that the quality of different pot seedlings in the same pot will be uneven after the cultivation of pot seedlings.If the pot seedlings are mechanically transplanted to the field environment without the process of quality classification and the removal of inferior seedlings,it will have a negative impact on the yield and quality of subsequent agricultural products.Therefore,took the tomato plug seedling as the research object,and based on the spectral technolo-gy and machine learning method,the test samples were rapidly nondestructive testing and classification recognition was re-alized.Firstly,the physiological and biochemical indexes and spectral data of samples were collected respectively,and the grade of each sample was corresponding to the spectral data according to The comprehensive quality classification crite-ria.Secondly,Multiple Scattering Correction(MSC)and Competitive Adaptive Reweighting(CARS)were used to pre-process and reduce the dimension of the acquired spectral data,which eliminated some redundant data and improves the reliability of the data.Finally,the quality classification model of plug seedlings was established by using random forest and BP neural network algorithm respectively,and the purpose of quality classification of plug seedlings was realized by spectral data.By comparing the evaluation indexes of the classification model,it can be concluded that the classification model constructed by the algorithm combined with MSC+CARS+BP neural network had the best recognition effect,its ac-curacy rate was 98.2%,and the mean of reconciliation was 98.5%,which can provide technical reference for rapid non-destructive testing of pot seedling quality in the future,in order to achieve selective transplanting of robust seedlings.关键词
穴盘苗/光谱检测/机器学习/随机森林/BP神经网络Key words
plug seedling/spectral detection/machine learning/random forest/BP neural network分类
物理学引用本文复制引用
汤来锋,桑伢员,姬江涛,许威广,杨旺..基于光谱技术的穴盘苗品质分类算法研究[J].农机化研究,2025,47(8):243-250,8.基金项目
国家自然科学基金项目(51975186) （51975186）
