https://scholars.lib.ntu.edu.tw/handle/123456789/438619
標題: | Evaluation of nitrogen content in cabbage seedlings using hyper-spectral images | 作者: | Chen C.-T. SUMING CHEN Wang C.-Y. Yang I.-C. Hsiao S.-C. Tsai, Chao-Yin |
公開日期: | 2008 | 卷: | 2 | 期: | 2 | 起(迄)頁: | 97-102 | 來源出版物: | Sensing and Instrumentation for Food Quality and Safety | 摘要: | Monitoring of nutrient status of crops is essential for better management of crop production. Nitrogen is one of the most important elements in fertilizer for the growth and yield of vegetable crops. In this study, nitrogen content of cabbage seedlings was evaluated using hyper-spectral images. Cabbage seedlings, cultured at five nitrogen fertilization levels, were planted in the 128-cell plug trays and grown in a phytotron at National Taiwan University. The images, ranged from 410 to 1,090 nm, of cabbage seedlings were analyzed by a hyper-spectral imaging system consisting of CCD cameras with liquid crystal tunable filters (LCTF), which was developed in this study. The digital images of seedling canopies were processed including image segmentation, gray level calibration and absorbance conversion. Models including modified partial least square regression (MPLSR), step-wise multi-linear regression (SMLR) and artificial neural network with cross-learning strategy (ANN-CL) were developed for the determination of the nitrogen content in cabbage seedlings. The three significant wavelengths derived from SMLR model are 470, 710, and 1,080; and the best result is obtained by ANN-CL model, in which r c = 0.89, SEC = 6.41 mg/g, r v = 0.87, and SEV = 6.96 mg/g. The ANN-CL model is more suitable for the remote sensing in precision agriculture applications because not only its model accuracy but also only 3 wavelengths are needed. © Springer Science+Business Media, LLC 2008. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/438619 | ISSN: | 19327587 | DOI: | 10.1007/s11694-008-9041-5 | SDG/關鍵字: | absorbance; Artificial neural network (ANNs); Cabbage seedlings; crop production; Digital imaging; Food inspection; Gray levels; Growth and yield; Hyperspectral image (HSI); Learning strategies; Liquid crystal tunable filters (LCTF); Multilinear regression (MLR); National Taiwan University (NTU); nitrogen contents; Nitrogen fertilization; nutrient status; Optical methods; Partial least square regression (PLSR); Vegetable crops; Backpropagation; C (programming language); Cameras; Canning; Charge coupled devices; Computer networks; Crops; Crystal filters; Cultivation; Curve fitting; Digital image storage; Food preservation; Food processing; Forestry; Image enhancement; Image processing; Image segmentation; Imaging systems; Imaging techniques; Least squares approximations; Light sources; Linear regression; Liquid crystals; Metropolitan area networks; Network protocols; Neural networks; Nitrogen; Nonmetals; Optical engineering; Optics; Optoelectronic devices; Regression analysis; Nitrogen fertilizers |
顯示於: | 生物機電工程學系 |
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