Early Classification and Detection of Melon Graft Healing State Based on Hyperspectral Imaging
YANG Jie-kai1, GUO Zhi-qiang1, HUANG Yuan2, 3*, GAO Hong-sheng1, JIN Ke1, WU Xiang-shuai2, YANG Jie1
1. College of Information Engineering, Hubei Key Laboratory of Broadband Wireless Communication and Sensor Network, Wuhan University of Technology, Wuhan 430070, China
2. College of Horticulture and Forestry Sciences, Key Laboratory of Horticultural Plant Biology, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China
3. Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Shenzhen 518000, China
Abstract: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 1~7 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.
Key words:Hyperspectral imaging; Melon grafting; Data preprocessing; Feature extraction; Classification and recognition model
杨杰锴,郭志强,黄 远,高宏盛,金 科,吴香帅,杨 杰. 高光谱成像的甜瓜嫁接愈合状态早期分类检测[J]. 光谱学与光谱分析, 2022, 42(07): 2218-2224.
YANG Jie-kai, GUO Zhi-qiang, HUANG Yuan, GAO Hong-sheng, JIN Ke, WU Xiang-shuai, YANG Jie. Early Classification and Detection of Melon Graft Healing State Based on Hyperspectral Imaging. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(07): 2218-2224.
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