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| 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
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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.
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Received: 2021-07-02
Accepted: 2021-08-25
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Corresponding Authors:
HUANG Yuan
E-mail: huangyuan@mail.hzau.edu.cn
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[1] Jiang Kai, Wang Jinwu, Li Cuiling. International Agricultural Engineering Journal, 2018, (4): 65.
[2] Jiang Kai, Chen Liping, Feng Qingchun, et al. International Agricultural Engineering Journal, 2020, 29(2): 250.
[3] LOU Jian-zhong, LI Jian-ping, ZHU Pan-an(楼建忠，李建平，朱盘安). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2016, 32(23): 64.
[4] SU Ying-xin, ZHANG Yue-feng, GU Song(苏颖欣，张跃峰，辜 松). Journal of Agricultural Mechanization Research(农机化研究), 2020, 42(4): 184.
[5] FU Qing, GUO Chen, LUO Wen-lang(付 青，郭 晨，罗文浪). Progress in Laser and Optoelectronics(激光与光电子学进展), 2020, 57(20): 202803.
[6] LIU Fang-yuan, HUANG Yuan, LI Xuan, et al(刘方园，黄 远，黎 煊，等). Chinese Melons and Vegetables(中国瓜菜), 2019, 32(3): 18.
[7] LIU Shuang, TAN Xin, LIU Cheng-yu, et al(刘 爽，谭 鑫，刘成玉，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析) , 2019, 39(11): 3540.
[8] XU Ying-chao, WANG Xiang-you, YIN Xiang, et al(许英超，王相友，印 祥，等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2018, 49(2): 339.
[9] ERSHAT Ablet, BAIDENGSHA Maimaitiaili, MAMAT Sawut, et al(依尔夏提·阿不来提，白灯莎·买买提艾力，买买提·沙吾提，等). Acta Optica Sinica(光学学报)，2019，39(9)：0930003.
[10] ZHANG Tian, LIAO He-ping, CUI Lin-lin(张 甜，廖和平，崔林林). Journal of Remote Sensing(遥感学报), 2017, 21(4): 604.
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