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首页> 外文会议>Autonomous air and ground sensing systems for agricultural optimization and phenotyping II >A predictive model for turfgrass color and quality evaluation using deep learning and UAV imageries
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A predictive model for turfgrass color and quality evaluation using deep learning and UAV imageries

机译：使用深度学习和无人机图像的草皮草颜色和质量评估的预测模型

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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.
机译：每天有数以百万计的美国人接触草皮草。通常，这被低估了，并且被看作是较大实体的视觉支持刺激，在住宅草坪（也提供休闲场所），商业景观，公园，运动场和高尔夫球场上可以发现数百万英亩的草皮草。除这些用途外，草皮草还为环境提供了许多功能上的好处，例如减少土壤侵蚀，冷却周围环境和固碳。但是，草皮草的迅速发展也引起了人们对自然资源保护和环境质量的警觉，最大的影响是水的消耗。本文提出了一种机器学习方法，可以帮助种植者和研究人员确定草皮草的整体质量和颜色等级，从而协助草皮草管理，包括优化灌溉水调度。 Google和NVIDIA的工具使您可以在个人计算机或可在小型无人飞行器（UAV）上使用的小型处理器上使用深度学习技术来训练模型。典型的评估过程是一个漫长而费力的过程，它本质上是主观的，因此经常会受到批评和关注。一种质量和颜色评估的计算方法将提供更快，更准确和更一致的评级，从而有助于提高灌溉用水效率。正在进行的研究的总体目标是使用深度学习技术和无人机图像进行草皮质量和颜色评估，并帮助所有利益相关者优化节水。

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