苹果花氮素含量是反映其质量高低的重要因素, 利用高光谱技术对苹果花氮素含量进行定量化反演, 可为苹果信息化管理提供理论依据。 在室内条件下, 利用ASD FieldSpec 3地物光谱仪, 测定了120个盛花期苹果花样品的高光谱反射率, 并化验了其氮素含量。 在分析苹果花原始光谱和一阶导数光谱特征的基础上, 与其氮素含量进行相关分析, 确定敏感波段, 构建特征光谱参数, 建立氮素含量预测模型, 对模型进行了优选和检验。 结果表明, 苹果花氮素含量与原始光谱反射率在374～696, 1 340～1 890, 2 052～2 433 nm波段呈极显著负相关, 在736～913 nm呈极显著正相关； 与一阶导数光谱反射率在637～675 nm呈极显著负相关, 在676～746 nm呈极显著正相关。 构建的6个特征光谱参数与苹果花氮素含量均呈极显著相关。 通过进一步比较和筛选, 确定了基于640 nm和676 nm原始光谱反射率的2个苹果花氮素含量最佳预测模型。 经检验, 模型决定系数R2分别为0.825 8和0.893 6, 平均预测精度达92.9%和94%。 研究成果为快速预测苹果花氮素含量及苹果的实时营养诊断提供了理论依据和技术支撑。

The present paper aims to quantitatively retrieve nitrogen content in apple flowers, so as to provide an important basis for apple informationization management. By using ASD FieldSpec 3 field spectrometer, hyperspectral reflectivity of 120 apple flower samples in full-bloom stage was measured and their nitrogen contents were analyzed. Based on the apple flower original spectrum and first derivative spectral characteristics, correlation analysis was carried out between apple flowers original spectrum and first derivative spectrum reflectivity and nitrogen contents, so as to determine the sensitive bands. Based on characteristic spectral parameters, prediction models were built, optimized and tested. The results indicated that the nitrogen content of apple was very significantly negatively correlated with the original spectral reflectance in the 374-696, 1 340-1 890 and 2 052-2 433 nm, while in 736-913 nm they were very significantly positively correlated; the first derivative spectrum in 637-675 nm was very significantly negatively correlated, and in 676-746 nm was very significantly positively correlated. All the six spectral parameters established were significantly correlated with the nitrogen content of apple flowers. Through further comparison and selection, the prediction models built with original spectral reflectance of 640 and 676 nm were determined as the best for nitrogen content prediction of apple flowers. The test results showed that the coefficients of determination (R2) of the two models were 0.825 8 and 0.893 6, the total root mean square errors (RMSE) were 0.732 and 0.638 6, and the slopes were 0.836 1 and 1.019 2 respectively. Therefore the models produced desired results for nitrogen content prediction of apple flowers with average prediction accuracy of 92.9% and 94.0%. This study will provide theoretical basis and technical support for rapid apple flower nitrogen content prediction and nutrition diagnosis.