Analysis of Nitrogen Content in Vegetables Using Intelligent Spectral Information
Date Issued
2007
Date
2007
Author(s)
Chen, Chia-Tseng
DOI
zh-TW
Abstract
Using spectral remote sensing to monitor the physiological status during growth has been attempted in the recent studies. In the work, the near infrared spectrophotometer (NIRS 6500, FOSS NIRSystems Inc.) and the hyper-spectral imaging system developed in this study were used to measure and analyze the reflectance spectra of vegetables in order to provide the basis for the future development of the on-line non-destructive remote sensing system for monitoring the nitrogen content of vegetable crops. The typical calibration models, including step-wise multilinear regression (SMLR) and modified partial least square regression (MPLSR), were adopted to examine the prediction performance of plant nitrogen content by using the spectral data firstly. Furthermore, the machine learning algorithms, including artificial neural network (ANN), real genetic algorithm (RGA), and information entropy (IE), were adopted to develop the intelligence-based calibration models to improve the prediction accuracy of calibration models.
In the first part of this dissertation, 113 samples of Chinese mustard (Brassica rapa L. var. chinensis (Rupr.) Olsson) were cultured by three different nitrogen fertilization treatments, and the reflectance spectra of leaves in terms of powder form were used to develop the calibration models. The results show that derivative treatments can reduce the noises of spectral shift caused by the particle sizes, and the significant wavelengths with high correlation coefficient ( |r| > 0.9 ) appear in the selected significant spectral band (1400-2450 nm). Regarding the nitrogen prediction accuracy, the SMLR model with smooth and first derivative pre-treatments and four significant wavelengths (2124, 2240, 1666, and 1632 nm) gives the best results (SEC = 2.059 mg/g, rc = 0.991, SEV = 2.131 mg/g, rv = 0.990). The results point out the SMLR model with a few wavelengths as inputs can be better than MPLSR model when spectral information is without water absorbance interference. Moreover, the SMLR model could be used to replace the time-consuming wet chemical method, such as Kjeldahl method, to analyze the nitrogen content in vegetable leaves. The results also indicate that a hyper-spectral imaging system, constructed of silicon CCD cameras and liquid crystal tunable filters (LCTF) using MPLSR method with the smooth and second derivative spectral information in range of 450 to1000 nm, could be used as the aids for nitrogen fertilization management of vegetable growth in the field.
In the second part, fresh leaves of cabbage seedlings (Brassica oleracea L.) after fertilizations with 5 different concentrations are used to measure the reflectance absorbance spectra. To develop a multi-spectral imaging system for remote sensing of the nitrogen content of crops, the significant wavelengths and calibration models were carefully evaluated in this study. The significant wavelengths in full band (400-2500 nm) and a selected band (450-950 nm), which is suitable for silicon CCD cameras, were investigated. Significant wavelengths for estimating nitrogen content of cabbage seedling leaves were first determined by SMLR analysis. A proposed ANN model with cross-learning scheme (ANN-CL) was further developed to increase the prediction accuracy. To comply with the design of a practical multi-spectral imaging system using silicon CCD cameras and commercially available bandpass filters, an ANN-CL model with four inputs of spectral absorbance at 490, 570, 600, and 680 nm was developed. The calibration results (rc = 0.93, SEC = 0.873%, and SEV = 0.960%) reduce the SEV about 15% when compared with the SMLR method with four wavelengths (SEV = 1.099%). In addition, the results are comparable to that of SMLR with seven wavelengths (rc = 0.94, SEC = 0.806%, and SEV = 0.993%) in the full band. These results indicate that the ANN model with cross-learning using spectral information at 490, 570, 600, and 680 nm could be used to develop a practical remote sensing system to predict nitrogen content of cabbage seedlings.
In the third part, the self-developed hyper-spectral imaging system, constructed from two sets of CCD cameras and liquid crystal tunable filters (LCTF, VIS and VNIR), were used to grab the spectral images of cabbage seedlings in the wavelength band of 410-1090 nm. In the analysis of hyper-spectral images, the region of seedling canopy was precisely extracted by image segmentation, which was dealt with a simply binary procedure, due to the fine spatial resolution of images. To calibrate and transfer the gray values of seedling canopy to the reflectance absorption, the six standard gray-blocks were used. The first and second significant wavelengths, analyzed by the information entropy (IE) index, are 650 nm and 690 nm, which are mutual different to the linear correlation (LC) analysis between nitrogen content and spectral data. The third significant wavelength of IE analysis is 530 nm, which is similar to 520 nm of LC. However, the fourth significant wavelength of LC is 470 nm, whose index value of IE is less than the wavelengths of 760 nm and 900 nm. The significant wavelengths of IE analysis are including 650, 690, 520, 760, and 900 nm. In the results of hyper-spectral calibration model analysis by using raw spectral data, MPLSR with six factors reduces the values of SEC and SECV to 6.20 mg/g and 7.64 mg/g respectively. Besides, the SMLR with three significant wavelengths (470, 1080, and 710 nm) gives the best results (SEC=7.55 mg/g, SEC=8.13 mg/g) by using simply linear equation.
The different significant wavelengths sets of LC, IE and SMLR are used as input data of the intelligence-based calibration models of RGA and ANN-CL to improve the prediction accuracy of nitrogen content analysis. Regarding the RGA analysis, the genetic population was generated randomly and the best fitness genetic population was kept to generate the next generation by crossover and mutation, and the global minimum of error was achieved. Therefore, the RGA calibration model with five significant wavelengths set (650, 690, 520, 760, and 900 nm) of IE is obtained with the good prediction results (SEV=7.79 mg/g). Moreover, the intelligence-based calibration model of ANN-CL with 3/4 sample selection ratio of the calibration set, using the same significant wavelengths set of RGA model, reduces the SEC to 6.47 mg/g and SEV to 5.76 mg/g effectively.
As a conclusion, the study has successfully developed nitrogen content prediction models using multi-spectra data of vegetable crops by integrating the near infrared, spectral images technology and artificial intelligence algorithms. With these research results, the remote sensing system with a multi-spectral imager could be developed for monitoring the nitrogen status of greenhouse crops in the future. The information of crops nitrogen status is useful for the precision management of nitrogen fertilization.
Subjects
鳳京小白菜
甘藍苗
近紅外光
光譜影像
類神經網路
基因遺傳演算法
Chinese Mustard
Cabbage Seedlings
Near Infrared (NIR)
Spectral Image
Artificial Neural Network (ANN)
Information Entropy (IE)
Genetic Algorithm (GA)
SDGs
Type
thesis
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