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6979565 
Journal Article 
Evaluation of nitrogen content in cabbage seedlings using hyper-spectral images - art. no. 67610L 
Chen, S; Chen, CT; Wang, C; Yang, IC; Hsiao, SC; Tu, SI 
2007 
Unk 
Proceedings of SPIE
ISSN: 0277-786X
EISSN: 1996-756X 
SPIE-INT SOC OPTICAL ENGINEERING 
BELLINGHAM 
PROCEEDINGS OF THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS (SPIE) 
6761 
L7610-L7610 
WOS:000253411600013 
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 1090 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 1080; 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. 
hyper-spectral images; nitrogen content; seedlings; artificial neural network 
Chen, YR; Meyer, GE; 
978-0-8194-6921-2 
Conference on Optics for Natural Resources, Agriculture, and Foods II 
Boston, MA