基于数码相机的水稻冠层图像分割及氮素营养诊断

利用数码相机对作物进行快速准确的营养诊断,需要对图像中作物冠层部分与非冠层部分进行有效的分割。该文依据绿色植被和土壤在可见光区域反射光谱的差异,提出了根据数字图像绿色通道和红色通道差值的大小设定阈值对图像进行分割的方法。阈值设定为10～20之间时对水稻冠层图像有较好的分割效果,拔节期和孕穗期获得最佳图像分割效果的阈值分别为10与20。分割后图像中提取的特征参数与SPAD值、叶片含氮量等指标间具有良好的相关关系,其中红光标准化值NRI与两者间的相关系数达到-0.87和-0.65。该方法能准确地分割水稻冠层图像,且简便易行,对绿色植被的图像分割具有普适性,有较高应用价值。

Digital camera-based image segmentation of rice canopy anddiagnosis of nitrogen nutrition

Segmenting canopy region effectively from the image captured by a digital camera is necessary for diagnosis of crop nutrition. An image segmentation thresholding method was proposed based on the difference of green and red channel (GMR) according to the difference of reflectance spectrum between vegetation and soil in visible band. The range of optimum threshold was from 10 to 20 during the rice growing stage, and the best threshold for shooting and booting stage was 10 and 20, respectively. The feature parameters of the segmentation of canopy images using this method are in good agreement with the SPAD readings, leaf nitrogen content, etc, in which the correlation coefficient between normalized redness intensity (NRI) and the two reached -0.87, -0.65, respectively. It demonstrates that this method is not only simple and accurate, but also applicable universally to other green crops.