基于Android手机平台的玉米叶片含氮量无损检测

为了提供一种玉米叶片含氮量无损快速检测方法,分析了玉米叶片的颜色特征参数与含氮量的关系,并基于Android手机平台开发了玉米叶片含氮量检测软件。首先获取包含被测玉米叶片与标定色块组的图像,利用标定色块对图像色彩进行校正,以减小外界光照等因素对图像色彩造成的失真。进而进行图像分割、图像平滑和颜色特征信息提取等处理,分析了各颜色特征参数与玉米叶片含氮量的关系,发现绿光标准化值与含氮量之间线性关系最好。应用Java语言和OpenCV计算机视觉库在Android手机平台上实现了玉米叶片的图像获取、图像处理和查看结果等功能。实验结果表明,该方法对玉米叶片含氮量的绝对测量误差为-0.40%~0.35%,均方根误差为0.20%,从采集图像到给出结果所用时间小于10 s。

Non-destructive and Rapid Detection Method on Nitrogen Content of Maize Leaves Based on Android Mobile Phone

Maize is widely planted in China and even in the world. Nitrogen is an essential nutrient for the growth and development of maize, which has a significant impact on maize yield. In order to provide a non-destructive and rapid detection method for nitrogen content of maize leaves, the relationship between the color characteristics and nitrogen content of maize leaves was analyzed, and a nitrogen content detection software for maize leaves was developed based on Android platform. The image containing the measured maize leaf and the calibration color block group (red, green, blue, white, black and grey) were obtained. In order to reduce the distortion caused by the external illumination and other factors, the image color was corrected by the calibration color block. After the image segmentation, image smoothing, and color feature information extraction, the relationship between the color features and the nitrogen content of the maize leaves was analyzed. The experimental results showed that the linear relationship between the green standard value and the nitrogen content was the best. Besides, Java programming language and OpenCV were applied to realize image acquisition, image processing and results viewing based on Android platform. The validation results indicated that the absolute error of the method for the nitrogen content of maize leaves was between -0.40% and 0.35%, and the root mean square error was 0.20%. The time from image collection to giving results was less than 10s. The proposed nitrogen detection method had the advantages of rapidity, economy and portability, and can be used for real-time detection on nitrogen content of maize leaves.