基于多光谱图像技术的番茄营养素诊断模型

为了快速、准确估测番茄营养水平和生长状态,利用多光谱图像分析技术研究了温室番茄营养素含量和图像特征的相关性。在日光条件下采集了温室番茄叶片多光谱图像,并采用多尺度Retinex算法有效地解决了叶片平整度差异造成的图像质量退化问题。从颜色模型、比值植被指数和归一化差值植被指数出发,自定义了49个多光谱图像特征参数。结合相关性分析和系统聚类分析消除了多光谱图像特征参数的多重共线性,并提取了4个能反映叶绿素含量(SPAD指数)和全氮含量预测模型,其中SPAD指数模型的决定系数(R2)为0.8668,均方根误差(RMSE)为3.997;全氮模型的R2为0.7284,RMSE为0.5130。

Diagnosis model of tomato nutrient content based on multispectral images

In order to estimate nutritional level and growth status of tomato leaves in greenhouse fast and accurately, the correlation between the nutrient content and every multispectral image feature was studied. The multispectral images of the tomato leaves were acquired in the natural sunlight condition, and then the multi-scale Retinex algorithm was adopted to reduce the imaging degradation caused by the nonflatness of the leaf surface. Based on color model, vegetation indices of NDVI and RVI, 49 characteristic parameters of multispectral images were defined and calculated. Correlation analysis and systematic cluster analysis were used to eliminate multivariate collinearity of the above-mentioned self-defined parameters and finally four optimal parameters were extracted. The stepwise multiple regression was used to develop the prediction models of the SPAD value and nitrogen content of tomato leaf. The result showed that the model had higher predictive ability. The R-Square and RMSE of SPAD model were 0.8668 and 3.997, and the R-Square and RMSE of nitrogen model were 0.7284 and 0.5130, respectively.