Evaluation of optical sensor technologies to optimize winter wheat (Triticum aestivum L.) management

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dc.contributor.author Lorence, Ashley Abigail
dc.date.accessioned 2017-11-17T19:39:10Z
dc.date.available 2017-11-17T19:39:10Z
dc.date.issued 2017-12-01 en_US
dc.identifier.uri http://hdl.handle.net/2097/38245
dc.description.abstract Sensor technology has become more important in precision agriculture, by real time sensing for site specific management to monitor crops during the season especially nitrogen (N). In Kansas N available in the soils can vary year to year or over a course of a year. The objective of this study was to compare current available passive (PS) and active optical sensor technologies (AOS) performance in regards to sky conditions effects and derive the NDVI (normalized difference vegetation index) relationship to wheat yield, as well as evaluate KSU optical sensor-based N recommendations against KSU soil test N recommendation system and sUAS (small unmanned aircraft systems) based recommendation algorithms with the PS and AOS platforms. Each year (2015-2016 & 2016-2017) five field trails across Kansas were conducted during the winter wheat crop year in cooperation with county ag agents, farmers, and KSU Agronomy Experiment Fields. Treatments consisted of N response curve, 1st and 2nd generation KSU N recommendation algorithms, sUAS based recommendation algorithms, and KSU soil test based N recommendations applied in the spring using N rates ranging from 0 to 140 kg ha⁻¹. Results indicate the Holland Scientific Rapid Scan and MicaSense RedEdge NDVI data was strongly correlated and generated strong relationships with grain yield at 0.60 and 0.57 R² respectively. DJI X3 lacks an NIR band producing uncalibrated false NDVI and no relationship to grain yield at 0.03 R². Calibrated NDVI from both sensors are effective for assessing yield potential and could be utilized for developing N recommendation algorithms. However, sensor based treatments preformed equal to higher yields compared the KSU soil test recommendations, as well as reduced the amount of fertilizer applied compared to the soil test recommendation. The intensive management algorithm was the most effective in determining appropriate N recommendations across locations. This allows farmers to take advantage of potential N mineralization that can occur in the spring. Further research is needed considering on setting the NUE (nitrogen use efficiency) in KSU N rec. algorithms for effects of management practice, weather, and grain protein for continued refinement. en_US
dc.language.iso en en_US
dc.publisher Kansas State University en
dc.subject Precision agriculture en_US
dc.subject Remote sensing en_US
dc.subject Nitrogen management en_US
dc.subject Winter wheat en_US
dc.title Evaluation of optical sensor technologies to optimize winter wheat (Triticum aestivum L.) management en_US
dc.type Thesis en_US
dc.description.degree Master of Science en_US
dc.description.level Masters en_US
dc.description.department Department of Agronomy en_US
dc.description.advisor Antonio R. Asebedo en_US
dc.date.published 2017 en_US
dc.date.graduationmonth December en_US

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