Research Of Corn Vegetation Fraction Based On UAV Visible Spectrum

Relationship between vegetation fraction and growing crops focus on the current research, it has important significance for crop production and management during the various stages of growth. This paper studies the corn vegetation fraction calculation methods and grain maize vegetation fraction temporal mapping.In order to obtain low altitude high-resolution visible spectral image information, This article designed a triaxial PTZ controller based on UAV mounted platform, the system uses STM32F429 embedded microcontroller as the master unit, camera using angular velocity message of the world’s first 6-axis motion processing sensor MPU6050 as speed loop feedback, through serial expansion bus communication with the head master unit, using a low-pass filtering for attitude algorithm. Experimental results show that the system mounted this PTZ can get jitter-free high-resolution images to meet the test requirementsIn precision agriculture, calculation vegetation fraction is a important aspect about agricultural information collection application. Aerial work tools such as planes and satellites and any more, because of the low spatial and temporal resolutions are not suitable for these applications. In this research, The UAV equipped with a commercial camera used to obtain the super-low-altitude visible spectrum images of corn grain crop. The camera visible spectrum images used to 3D photo-reconstruction in the all corn experimental area, then the part of vegetation can be extracted from then 3D photos to get pure vegetation portion of pixels to calculate vegetation fraction. The 3D photo-reconstruction images quality directly affects the accuracy of corn vegetation fraction, in order to verify the pros and cons of 3D photo-reconstruction images impress on vegetation fraction, Particularly choose the test area of 16 square meters of the four calibration as the sampling process, the use of sampling images calibration K-Means method to calculate the extraction of corn vegetation fraction. Finally, to estimate the value of the entire test area fraction by four samples of vegetation fraction calibration value calculation, and then apply for the goodness of fit evaluating the vegetation fraction value of 3D photo-reconstruction. The results show, it has small relative error using goodness of fit to assess sampling calibration to calculate vegetation fraction, this method can satisfy the requirements of the test.Vegetation fraction temporal mapping assessment by the vegetation index, This research selected the following ten vegetation indices:normalized green-red difference index、excess green index、cive vegetation index、vegetativen index、excess green minus excess red index、Woebegone index、visible-band difference vegetation index、red-green ratio index、normalized green-blue difference index and green and red index. Through these ten kinds of vegetation indices image extraction results by selecting two of vegetation indices of maize grain of vegetation fraction temporal mapping calculation and assessment, while the flying height of 10 meters and 15 meters of grain and 35 day to 48 day between the exact classification of vegetation were studied. The results showed that, EXG and VEG is to achieve vegetation fraction map has two vegetation indices the best accuracy at an altitude of 10 meters accurate evaluation index values between 83.54% and 87.35%, while the 15 meters in 81.42% and 85.68% between. These indices vegetation map of the entire corn field grain of accuracy in space and time are unified.