关键词: 氮磷互作 , 冠层光谱 , PNN , 营养诊断 , 水稻

Abstract: 【Objective】Nitrogen (N) and phosphorus (P) are macronutrients for crops and the diagnosis of N and P in crops is the premise of scientific fertilization. Accurate, fast, and nondestructive detection of the deficiency of N and P in rice has a great meaning on precision fertilization, cost-saving and agricultural non-point source pollution control.【Method】A two-factor pot experiment of N (6 levels) and P (2 levels) was carried out, canopy spectral reflectance and plant TN and TP content were measured simultaneously at tillering, jointing and heading stages. The interactive effects of N and P on rice growth (N and P content) and canopy reflectance at 350-1 330 nm was investigated and PNN model was used to classify the N and P levels based on the canopy reflectance. In order to avoid the error of different batches caused by instrument, light, wind, temperature, water and other environmental conditions, the reflectance spectra data were standardized. A total of 2/3 of the data were used to train the PNN model and the other 1/3 data were used to test the PNN model. 【Result】 Rice N content was significantly influenced by the N rate, P rate and the interaction of N and P. But rice P content was only affected by P rate and N rate, the interaction of N and P did not exist. The response of canopy reflectance spectra to N rate was not influenced by P rates, and N deficiency increased the reflectance at visible band and decreased those in the near infrared region. Under P-deficiency, the reflectance at near-infrared bands decreased at all N levels, but the reflectance at visible bands increased in N application treatments while declined at tillering stage, increased at jointing stage, then decreased at heading stage when the N was seriously deficient. The identification accuracy of PNN model was highest at jointing stage and lowest at heading stage. The identification accuracy at tillering and jointing stages was 83% and 94% for P levels, and 78% and 88% for N and P deficiency levels, respectively. However, the identification accuracy for N levels and interaction of N-P levels was only 61%-75%. It was noted that all the N-deficient treatments were not identified as P-deficiency and vice versa at tillering and jointing stages, which showed that PNN model could distinguish N-deficiency from P-deficiency.【Conclusion】Rice canopy reflectance was influenced by N and P fertilizer levels. The PNN model of the rice canopy reflectance can not only identify nitrogen and phosphorus fertilizer levels based on single factor, but also can distinguish N-deficiency from P-deficiency, and has important meaning and value in rice fertilization decision and may avoid yield and economic loss and environmental pollution caused by improper fertilization strategy.

Key words: nitrogen and phosphorus interaction , canopy spectra , PNN , nutrition diagnosis , rice