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Automation of images registration and stitching is one of the most important key technologies to the wide use of the low-altitude remote sensing by Micro-UAVs (unmanned aerial vehicles) in rice growing. In order to overcome the limitations, i.e. the thresholds need to be artificially determined for the traditional Harris corner detection algorithm, this paper proposed a self-adaptive algorithm for Harris corner detection, which was used in image registration and stitching of the rice low-altitude remote sensing. The algorithm was improved based on the traditional Harris corner detection algorithm by using a self-adaptive threshold determination method, which calculated from the standard deviation of image pixel gray-scale value. And then the characteristics of image were described by corners, and the images were registered by using the Euclidean distance among descriptors. In order to verify the effectiveness of the algorithm and optimize the relevant parameters, a verification test was conducted based on low-altitude remote sensing images, which were gained by a multispectral camera mounted on a multi-rotor unmanned helicopter during rice tillering stage. Four indices, the repetition rate (a measure of the stability of corner detection), the recognition rate (a measure of corner recognizable description operator), the registration rate (a measure of the accuracy of image registration and stitching) and running time of algorithm (a measure of computing speed of the algorithm), were proposed to evaluate the results of registration and stitching. Sixty images were randomly divided into 3 groups for verification test. Test results showed that the average registration rate reached 98.95%, and also the average repetition rate reached 96%, which indicated that the proposed algorithm had high accuracy. The repetition rate and the difference in image registration rates among the groups were not significant (at 0.05 significance level), which indicated that the proposed algorithm was stable and reliable. And the recognition rate among the groups was significant, and it indicated that the proposed algorithm had higher distinguishability to the remote sensing images, which was conducive to the precision of the automation of images registration and stitching. Threshold value of the proposed algorithm, which is the standard deviation of the image pixel gray values after standardization, here is set to 1 and 2 for optimization test. Test results showed that the registration rate was not significant, namely there was no significant difference (at 0.05 significance level) when the threshold value was equal to 1 or 2. However, comparing the average running time of the proposed algorithm, it showed that the running time when the threshold value was 1, is 2.5 times that when the threshold value was 2. Based on comprehensive consideration of the registration rate, the running time and the efficiency, the threshold value of 2 can be set as the optimum parameter of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The wind field parameters on rice canopy which formed by rotor airflow are significantly difference among different structure of unmanned helicopter. There are direct influence among the seed setting rate, operating efficiency, and the parameters of wind field on rice canopy. To explore the optimization parameters when the uniaxial single-rotor electric unmanned helicopter (USREUH) conducted supplementary pollination, orthogonal tests of three factors (including flight operating load, altitude and speed) and three levels was developed to measure the wind field which produced by USREUH in this study. The mass of tested USREUH is 9.3 kg, its rotor diameter is 2 m, and its maximum payload is 15 kg. The measured parameters of wind field included the following: wind speed at X direction (parallel to the direction of flight heading), Y direction (perpendicular to the direction of flight heading), and Z direction (the vertical direction), and also the battery's voltage drop at each takeoff and landing of USREUH to estimate its economy. The wind field parameters of the USREUH were measured by a wireless wind speed sensor network measurement system (WWSSN), which consists of several wireless wind speed sensors (WWSS), a flight global position system (FGPS), and an intelligent control focus node (ICFN). The WWSSN is a star topology, which can realize multi-point, multi-direction, mobile, and real-time measurement for wind field parameters on rice canopy, and also can record the pose information of the USREUH when the supplementary pollination is conducted. In order to reduce the affect from natural wind speed, serveral treatment rules about natural wind speed were adopted before wind field data analysis. The test results showed that: the maximum width of wind field produced by USREUH (peak wind speed >1 m/s at Y direction) was reached 8.1 m, it was indicated that this model of USREUH could meet the basic needs of supplementary pollination of hybrid rice under the designed test conditions. The peak wind speed which produced by USREUH was mainly affected by the following factors: flight operating load, altitude and speed. The lower altitude and flight speed, along with the increasing payload, it the higher peak wind speed was expected. Considering the width of wind field and battery electricity consumption, the order of the three influence factors is flight speed, takeoff weight, and flight height. And the optimal operation parameters for supplementary pollination in hybrid rice breeding are flight speed of 1.56 m/s, takeoff weight of 14.05 kg, and flight height of 1.93 m, respectively. The conclusions provide the important reference for the studies of operation parameters optimization based on any other kinds of USREUH, which are useful to the form of specifications for supplementary pollination in hybrid rice breeding using USREUH. [ABSTRACT FROM AUTHOR]