8 May 2017 A predictive model for turfgrass color and quality evaluation using deep learning and UAV imageries
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Millions of Americans come into contact with turfgrass on a daily basis. Often undervalued and seen as visual support stimulus for a larger entity, millions of acres of turfgrass can be found on residential lawns (which also provides an area for recreation), commercial landscape, parks, athletic fields, and golf courses. Besides these uses, turfgrass provides many functional benefits to the environment, such as reducing soil erosion, cooling its surrounding area, and soil carbon sequestration. However, rapidly expanding uses of turfgrass have also raised alarm for natural resources conservation and environmental quality, the largest impact being water consumption. This paper presents a machine learning approach that can assist growers and researchers in determining the overall quality and color rating of turfgrass, thereby assisting in turfgrass management including optimized irrigation water scheduling. Tools from Google and NVIDIA enable models to be trained using deep learning techniques on personal computers or on small form factor processors that can be used aboard small unmanned aerial vehicles (UAVs). The typical evaluation process is a long, laborious process, which is subjective by nature, and thus often exposed to criticism and concern. A computational approach to quality and color assessment will provide faster, accurate, and more consistent ratings, which in turn will help increase irrigation water use efficiency. The overall goal of the ongoing research is to use deep learning techniques and UAV imageries for the turfgrass quality and color assessment and help all the stakeholders to optimize water conservation.
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Claude Phan, Claude Phan, Amar Raheja, Amar Raheja, Subodh Bhandari, Subodh Bhandari, Robert L. Green, Robert L. Green, Dat Do, Dat Do, "A predictive model for turfgrass color and quality evaluation using deep learning and UAV imageries", Proc. SPIE 10218, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II, 102180H (8 May 2017); doi: 10.1117/12.2262042; https://doi.org/10.1117/12.2262042

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