Current Issue : January - March Volume : 2019 Issue Number : 1 Articles : 5 Articles
GNSS-R (Global Navigation Satellite System-Reflectometry) has been demonstrated to\nbe a new and powerful tool to sense soil moisture in recent years. Multi-antenna pattern and\nsingle-antenna pattern have been proposed regarding how to receive and process reflected signals.\nGreat efforts have been made concerning ground-based and air-borne observations. Meanwhile,\na number of satellite-based missions have also been implemented. For the in-depth study of soil\nmoisture remote sensing by the technique of GNSS-R, regardless of the extraction methods of the\nreflected signals or the types of the observation platform, three key issues have to be determined:\nThe specular reflection point, the spatial resolution and the detection depth in the soil. However,\nin current literatures, there are no comprehensive explanations of the above three key issues.\nThis paper conducts theoretical analysis and formula derivation, aiming to systematically and\nquantitatively determine the extent of soil moisture being detected in three dimensions from the\nabove-mentioned aspects. To further explain how the three factors behave in the specific application,\nthe results of two application scenarios are shown: (1) a ground-based GPS measurement in\nMarshall, Colorado, US from the Plate Boundary Observatory, corresponding to single-antenna\npattern. The relative location of the specular reflection points, the average area of the First Fresnel\nEllipse Clusters and the sensing depth of the time-series soil moisture are analyzed, and (2) an\naviation experiment conducted in Zhengzhou to retrieve soil moisture content, corresponding\nto the multi-antenna pattern. The spatial distribution of soil moisture estimation with a certain\nresolution based on the flight tracks and the relevant sensing depth are manifested. For remote\nsensing using GNSS reflected signals, BeiDou is different from GPS mainly in the carrier frequency.\nTherefore, the results of this study can provide references for Chinaâ??s future development of the\nBeiDou-R technique....
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This work aims to study and compare different range finders applied to altitude sensing on\na rotating wings UAV. The specific application is the altitude maintenance for the fluid deployment\nvalve aperture control in an unmanned pulverization aircraft used in precision agriculture.\nThe influence of a variety of parameters are analyzed, including the tolerance for crop inconsistencies,\ndensity variations and intrinsic factors to the process, such as the pulverization fluid interference in\nthe sensorâ??s readings, as well as their vulnerability to harsh conditions of the operation environment.\nFiltering and data extraction techniques were applied and analyzed in order to enhance the\nmeasurement reliability. As a result, a wide study was performed, enabling better decision making\nabout choosing the most appropriate sensor for each situation under analysis. The performed data\nanalysis was able to provide a reliable baseline to compare the sensors. With a baseline set, it was\npossible to counterweight the sensors errors and other factors such as the MSE for each environment\nto provide a summarized score of the sensors. The sensors which provided the best performance in\nthe used metrics and tested environment were Lightware SF11-C and LeddarTech M16....
Seed size plays a large role in determining productivity of large seeded legumes. Inmany large seeded legumes such as pea and bean,\nactual yield, defined here as grain yield at harvestminus the weight of seed planted, is often a better measure of actual productivity\nthan grain yield at harvest, because the weight of planted seed varies with seed size. In many grain legumes, the weight of planted\nseed can be equal to 10% of the total grain yield, and minimizing the weight of planted seed could significantly impact actual yield.\nThis study produced an algorithm to examine the relationship between seed size, yield, and actual yield in silico.The output of this\nalgorithm predicted the ideotype for seed size in peas to bemuch lower (12.5 g./100 seeds) than the seed size of nearly all commercial\nvarieties, indicating that efficiency in pea cropping systems could be increased by reducing seed size.Modifications to the algorithm\nwould allow the prediction of the ideal seed size in other legumes.The algorithm predicts that there is likely very little correlation\nbetween seed size and grain yield, although larger seeded legumes will likely have a higher harvest index. Plant breeders can use\nthe ideotype predicted by this function to create varieties of peas and other large seeded legumes that have higher actual yield.The\nideotype for seed size was defined in pea....
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