Use of unmanned aerial vehicle extracted data to predict health and tiller count in wheat

Alexandre Brice Cazenave; Joseph Oakes; Wade Thomason; Maria Balota

doi:10.1117/12.2518725

14 May 2019 Use of unmanned aerial vehicle extracted data to predict health and tiller count in wheat

Alexandre Brice Cazenave, Joseph Oakes, Wade Thomason, Maria Balota

Proceedings Volume 11008, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV; 110080X (2019) https://doi.org/10.1117/12.2518725
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

Wheat, third most important cereal in the world, is sensitive to nitrogen deficiency. To increase yield, nitrogen (N) inputs are used but production costs may exceed returns if unnecessary applications are made; and the environment may become polluted. To improve N management, farmers of the mid-Atlantic generally apply N to wheat based on actual plant growth by counting the number of tillers or N concentration in the plant tissues. Both methods can be labor intensive and time consuming, and tissue testing also requires additional production costs. Remote-sensing technologies and more particularly Unmanned Aerial Vehicle (UAV) systems are now being used to extract new variables (spectral reflectance and vegetation indices) and to estimate plant growth and N requirements. Previous studies in Virginia have shown that spectral reflectance data, collected using the ground GreenSeeker® system, could be used to estimate the number of tillers and tissue nitrogen content. The objective of this project was to evaluate the accuracy of remote sensing and UAV-based wheat spectral reflectance for estimating tiller density in winter wheat. Tillers were counted regularly and simultaneously with ground (using handheld GreenSeeker®) and aerial (using UAV) NDVI measurements. Each UAV flight was performed using a Red Green Blue (RGB) and Tetracam (Near InfraRed) camera to extract NDVI and color space indices. Our results showed significant correlations between the number of tillers and aerial indices but further analysis is needed to identify the best flight time for estimating wheat tiller density and early season N requirements.

Citation Download Citation

Alexandre Brice Cazenave, Joseph Oakes, Wade Thomason, and Maria Balota "Use of unmanned aerial vehicle extracted data to predict health and tiller count in wheat", Proc. SPIE 11008, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV, 110080X (14 May 2019); https://doi.org/10.1117/12.2518725

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KEYWORDS

Unmanned aerial vehicles

Nitrogen

Reflectivity

Remote sensing

RGB color model

Cameras

Near infrared

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