Study on Quantitative Detection of Tomato Seedling Robustness
in Spring Seedling Transplanting Period Based on VIS-NIR
Spectroscopy
JI Jiang-tao1, 2, LI Peng-ge1, JIN Xin1, 2*, MA Hao1, 2, LI Ming-yong1
1. College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
2. Henan Collaborative Innovation Center for Advanced Manufacturing of Mechanical Equipment, Luoyang 471003, China
Abstract：To screen the key indicators that affect the robustness of tomato plug seedlings during the spring nursery and transplanting period and realize its rapid non-destructive testing, this paper measured 5 seedling indicators, then used vector normalization and the independence weight coefficient method to determine each indicator. According to the weighting results, two indicators containing more comprehensive information and greater influence are selected: chlorophyll and dry quality. The simplified seedling evaluation value composed of the two indicators can approximate the comprehensive evaluation value. The correlation coefficient is 0.92, which greatly reduces the number of indicators required for quality testing, and can well represent the robustness of tomato seedlings during the spring seedling transplanting period. At the same time, the visible-near infrared spectrum data of each plug seedling is extracted and pre-processed by denoising and multi-scattering correction (MSC). This way, it can eliminate the spectral interference information caused by light scattering and make it more usable than the original spectral information. Subsequently, the spectrum-physical and chemical value symbiosis distance (SPXY) algorithm is used to divide the sample set. The distance between the samples is calculated using two variables of the band value and the evaluation value to maximize the characterization of the sample distribution to improve the difference and representativeness of the samples. Secondly, the competitive adaptive weighting algorithm (CARS) and the uninformative variable elimination-successive projections algorithm (UVE-SPA) are used to optimize the spectral feature wave number, reduce the spectral data dimension and obtain simplified spectral information that can better reflect the spectral characteristics and reduce redundancy. Finally, partial least squares-support vector machine (LS-SVM) and convolutional neural network (CNN) based on U-Net model transformation are applied. After extracting the characteristic wavelength, the preprocessed spectral data and the spectral data are respectively used as the input of the model and established a non-linear mapping model of spectral data and comprehensive evaluation value. We can carry out comparison and selection. The results show that the spectral information of the bands filtered by the UVE-SPA preprocessing method is more abundant and effective. The regression effects of the models built for the two preprocessed optimal bands are overall better than the models built for the full bands; the modeling effect of the CNN model is overall. It is better than the LS-SVM model, and the UVE-SPA-CNN model has the best effect on the regression analysis of spectral data and seedling evaluation values. The correlation coefficients of the modeling set and prediction set are 0.988 and 0.946, respectively, and the values of the root mean square error are 0.085 and 0.025, respectively, which provide a theoretical basis for directly using spectral data to obtain the evaluation value of tomato seedlings that incorporate multiple factors, thereby judging the robustness of the seedlings.
姬江涛，李鹏阁，金 鑫，马 淏，李明勇. 基于光谱的春季育苗移栽期番茄苗健壮度量化检测[J]. 光谱学与光谱分析, 2022, 42(06): 1741-1748.
JI Jiang-tao, LI Peng-ge, JIN Xin, MA Hao, LI Ming-yong. Study on Quantitative Detection of Tomato Seedling Robustness
in Spring Seedling Transplanting Period Based on VIS-NIR
Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1741-1748.
[1] HAN Lü-hua, MAO Han-ping, HU Jian-ping, et al(韩绿化, 毛罕平, 胡建平, 等). Transactions of The Chinese Society for Agricultural Machinery(农业机械学报), 2016, 47(11): 59.
[2] CAO Ling-ling, TIAN Ya-nan, ZHAO Li-qun et al(曹玲玲, 田雅楠, 赵立群, 等). Agricultural Engineering Technology(农业工程技术), 2017, 37(19): 15.
[3] GONG Bin-bin, WANG Ning, ZHANG Tie-jun, et al(宫彬彬, 王 宁, 章铁军, 等). Transactions of the Chinese Society for Agricultural Engineering(农业工程学报), 2019, 35(8): 237.
[4] HE Shi-xing, HE Di, XU Chun-lin，et al(何诗行, 何 堤, 许春林, 等). Transactions of The Chinese Society for Agricultural Machinery(农业机械学报), 2017, 48(12): 319.
[5] YANG Si, GAO Wan-lin, MI Jia-qi，et al(杨 斯, 高万林, 米家奇, 等). Transactions of The Chinese Society for Agricultural Machinery(农业机械学报), 2019, 50(S1): 128.
[6] DING Yong-jun, ZHANG Jing-jing, LI Xiu-hua, et al(丁永军, 张晶晶, 李修华, 等). Transactions of The Chinese Society for Agricultural Machinery(农业机械学报), 2016, 47(3): 292.
[7] Xie Chuanqi, Yang Ce, Moghimi Ali. Proc. SPIE, 2017, 10203: 1021305.
[8] SHAO Yuan-yuan, WANG Yong-xian, XUAN Guan-tao, et al(邵园园, 王永贤, 玄冠涛, 等). Transactions of The Chinese Society for Agricultural Machinery(农业机械学报), 2020, 51(8): 344.
[9] Song Xiangzhong, Huang Yue, Tian Kuangda, et al. Optik, 2020, 203: 164019.
[10] ERSHAT Abulaiti, BAIDENGSHA Maimaitiaili, MAMAT Sawut(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): 372.
[11] WANG Yang, XIAO Wen, ZOU Huan-cheng, et al(王 洋, 肖 文, 邹焕成, 等). Journal of Shenyang Agricultural University(沈阳农业大学学报), 2018, 49(1): 121.
[12] Wei Xuan, He Jincheng, Zheng Shuhe, et al. Infrared Physics and Technology, 2020, 105: 103099.
[13] GUAN Shan, PANG Hong-yang, SONG Wei-jie, et al(关 山, 庞弘阳, 宋伟杰, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2018, 34(14): 61.
[14] XIE Zhong-hong, XU Huan-liang, HUANG Qiu-gui, et al(谢忠红, 徐焕良, 黄秋桂, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2019, 35(13): 277.
[15] Zhang Bin, Miao Yanjie, Tian Yongzhi, et al. Journal of Nuclear Science and Technology, 2021, 58(7): 787.
