中文摘要：

为了精确、快速和稳定的对番茄氮、磷、钾3种元素的营养水平进行诊断,该文提出利用反射光谱技术诊断方法,选用遗传算法优选波段;采用主成分分析方法提取敏感波长下的纹理特征;通过逐步回归、主成分回归、偏最小二乘法回归分别建立基于光谱和图像特征的番茄叶片氮、磷、钾素模型。针对单一技术不能全面反映叶片营养信息的问题,采用人工神经网络对光谱和图像技术进行特征层的信息融合,建立了多信息融合的诊断模型,求得氮、磷、钾的相关系数R分别为0.9651、0.9216、0.9353;均方根误差RMSE分别为0.19、0.33、0.29。结果表明采用光谱与图像的融合技术模型比单一光谱模型提高的精度分别为6.25%、3.97%、7.92%,比单一图像模型提高的精度为3.80%、5.43%、3.26%,有更好的诊断作用,能够实现对番茄作物氮、磷、钾素营养水平的高精度快速检测。

英文摘要：

Because of the short growth cycle, large yield and high fertilizer requirements of facility crop, which were the characteristics causing high cost and high complement in cultivation medium, nitrogen (N), phosphorus (P) and Potassium (K) deficiencies frequent occurrence in the growth of facility crops. Accurate monitoring and diagnosis of nutrient content in facility crops during the growth process was very important-. In order to diagnose tomato nitrogen, phosphorus and potassium nutrition level more accurately, rapidly and stably, in the aspect of the spectral diagnostics technology, the changes of reflectance on characteristic wavelengths were taking into consideration to assess the nutritional status of crops.-. Sensitive bands were selected by using genetic algorithms. Then, the quantitative models of tomato nitrogen, phosphorus, potassium were established via stepwise regression, principal component regression and partial least squares method respectively based on reflectance spectra. The results verified that the stepwise regression models outperformed the principal component and partial smallest squares regression models of nitrogen and phosphorus, while principal component regression get the best models of phosphorus. The correlation coefficient R of the best models were nitrogen (0.9026) >phosphorus (0.8819) > potassium (0.8561) . The root mean square error (RMSE) were nitrogen (0.3191)