文献类型： 外文期刊

作者： Yang Jie-kai ¹ ; Guo Zhi-qiang ¹ ; Huang Yuan ² ; Gao Hong-sheng ¹ ; Jin Kei ¹ ; Wu Xiang-shuai ² ; Yang Jie ¹ ;

作者机构： 1.Wuhan Univ Technol, Coll Informat Engn, Hubei Key Lab Broadband Wireless Commun & Sensor, Wuhan 430070, Peoples R China

2.Huazhong Agr Univ, Coll Hort & Forestry Sci, Key Lab Hort Plant Biol, Minist Educ, Wuhan 430070, Peoples R China

3.Chinese Acad Agr Sci, Agr Genom Inst Shenzhen, Shenzhen Branch,Minist Agr, Guangdong Lab Lingnan Modern Agr,Genome Anal Lab, Shenzhen 518000, Peoples R China

关键词： Hyperspectral imaging; Melon grafting; Data preprocessing; Feature extraction; Classification and recognition model

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.609； 五年影响因子：0.516 ）

ISSN： 1000-0593

年卷期： 2022 年 42 卷 7 期

页码：

收录情况： SCI

摘要： The purpose of grafting is to improve the ability of plants to resist soil-borne diseases and abiotic stresses. The early detection of the grafting healing state of melon is an important demand for the current industrial development of nursery plants. Based on the standard normal variable transformation savitzky Golay smoothing second derivative (SNV-SG-SD) preprocessing, this paper proposes a competitive adaptive reweighting (DIS-CARS-SPA) feature extraction algorithm fusing grafting difference information. Establishes a radial basis function support vector machine ( GS-RBF-SVM) classification model based on grid optimization, The early classification detection of melon grafting healing state based on hyperspectral imaging was realized. Firstly, hyperspectral images of grafted survival seedlings and non-survival seedlings with pumpkin as rootstock and melon as scion were collected within 1 7 days of the healing period. Nine spectral preprocessing methods, two feature extraction algorithms and five optimization algorithms, and four kernel function support vector machine (SVM) classification models were used for analysis. The results show that the best is SNV-SG-SD spectral preprocessing, DIS-CARS-SPA feature extraction and GS-RBF-SVM classification model. Further analysis using the model shows that the classification accuracy of different types of binary classification on the same day can reach more than 99% on any day within 1 similar to 7 days of the healing period. More than 90.17% of the grafted seedlings survived on different days; More than 97.03% of the grafted non-survival seedlings could be classified on different days. On different days and types of 14 classifications, it can reach 96.85% which is 0.59% higher than the cars-spa feature extraction method without fusion of grafting difference information and 3.37% higher than the method without only preprocessing feature extraction. The results show that the proposed method can not only realize the two classifications of grafted survival seedlings and non-survival seedlings on the same day but also the two classifications of the same type on different days and the multi-classification of different types on different days. In practical application, it can advance the classification time to the first day after grafting (3 4 days for naked-eye observation and 1 2 days for machine vision technology). At the same time, the third day is the difference between mutation days of grafted survival seedlings and non-survival seedlings. The state of grafted survival seedlings can be divided into three stages: weak, medium strong, and the state of non-survival seedlings can be divided into two stages: weak weaker. This conclusion can provide effective guidance for the production of grafted melon seedlings and has a certain theoretical and practical value.