Research on a nondestructive model for the detection of the nitrogen content of tomato

Xiaodong Zhang; Chaohui Duan; Yafei Wang; Hongyan Gao; Lian Hu; Xinzhong Wang

doi:10.3389/fpls.2022.1093671

Research on a nondestructive model for the detection of the nitrogen content of tomato

Front Plant Sci. 2023 Jan 11:13:1093671. doi: 10.3389/fpls.2022.1093671. eCollection 2022.

Authors

Xiaodong Zhang^{1

2}, Chaohui Duan^{1

2}, Yafei Wang^{1

2}, Hongyan Gao^{1

2}, Lian Hu³, Xinzhong Wang^{1

2}

Affiliations

¹ College of Agricultural Engineering, Jiangsu University, Zhenjiang, China.
² Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, Jiangsu University, Zhenjiang, Jiangsu, China.
³ Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou, China.

Abstract

The timely detection of information on crop nutrition is of great significance for improving the production efficiency of facility crops. In this study, the terahertz (THz) spectral information of tomato plant leaves with different nitrogen levels was obtained. The noise reduction of the THz spectral data was then carried out by using the Savitzky-Golay (S-G) smoothing algorithm. The sample sets were then analyzed by using Kennard-Stone (KS) and random sampling (RS) methods, respectively. The KS algorithm was optimized to divide the sample sets. The stability competitive adaptive reweighted sampling (SCARS), uninformative variable elimination (UVE), and interval partial least-squares (iPLS) algorithms were then used to screen the pre-processed THz spectral data. Based on the selected characteristic frequency bands, a model for the detection of the nitrogen content of tomato based on the THz spectrum was established by the radial basis function neural network (RBFNN) and backpropagation neural network (BPNN) algorithms, respectively. The results show that the root-mean-square error of correction (RMSEC) and root-mean-square error of prediction (RMSEP) of the BPNN model were respectively 0.1722% and 0.1843%, and the determination coefficients of the correction set (R_c ²) and prediction set (R_p ²) were respectively 0.8447 and 0.8375. The RMSEC and RMSEP values of the RBFNN model were respectively 0.1322% and 0.1855%, and the R_c ² and R_p ² values were respectively 0.8714 and 0.8463. Thus, the accuracy of the model established by the RBFNN algorithm was slightly higher. Therefore, the nitrogen content of tomato leaves can be detected by THz spectroscopy. The results of this study can provide a theoretical basis for the research and development of equipment for the detection of the nitrogen content of tomato leaves.

Keywords: N; characteristic band; nondestructive detection; terahertz spectroscopy; tomato.

Grants and funding

This research was funded by Project of Agricultural Equipment Department of Jiangsu University (NZXB20210106). Key Laboratory of Modern Agricultural Equipment and Technology(Jiangsu University), Ministry of Education (Grant No. MAET202111). National Key Research and Development Program for Young Scientists (2022YFD2000013). Key Laboratory of Modern Agricultural Equipment and Technology (Ministry of Education), High-tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province (Grant No. JNZ201901). Scientific and Technological Project of Henan Province (Grant No. 212102110029).The National Natural Science Foundation of China (61771224 and 32071905).