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Application of machine learning for the evaluation of turfgrass plots using aerial images

机译：机器学习在利用航空影像评估草皮草样地中的应用

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Historically, investigation of turfgrass characteristics have been limited to visual ratings. Although relevant information may result from such evaluations, final inferences may be questionable because of the subjective nature in which the data is collected. Recent advances in computer vision techniques allow researchers to objectively measure turfgrass characteristics such as percent ground cover, turf color, and turf quality from the digital images. This paper focuses on developing a methodology for automated assessment of turfgrass quality from aerial images. Images of several turfgrass plots of varying quality were gathered using a camera mounted on an unmanned aerial vehicle. The quality of these plots were also evaluated based on visual ratings. The goal was to use the aerial images to generate quality evaluations on a regular basis for the optimization of water treatment. Aerial images are used to train a neural network so that appropriate features such as intensity, color, and texture of the turfgrass are extracted from these images. Neural network is a nonlinear classifier commonly used in machine learning. The output of the neural network trained model is the ratings of the grass, which is compared to the visual ratings. Currently, the quality and the color of turfgrass, measured as the greenness of the grass, are evaluated. The textures are calculated using the Gabor filter and co-occurrence matrix. Other classifiers such as support vector machines and simpler linear regression models such as Ridge regression and LARS regression are also used. The performance of each model is compared. The results show encouraging potential for using machine learning techniques for the evaluation of turfgrass quality and color.
机译：从历史上看，草皮草特性的研究仅限于视觉评级。尽管此类评估可能会产生相关信息，但由于收集数据的主观性，最终推断可能会令人怀疑。计算机视觉技术的最新进展使研究人员能够从数字图像中客观测量草皮特征，例如地面覆盖率，草皮颜色和草皮质量。本文重点研究从航空影像中自动评估草皮草质量的方法。使用安装在无人驾驶飞行器上的摄像头，收集了几种质量不同的草坪草地块的图像。这些地块的质量也根据视觉评价进行评估。目标是使用航拍图像定期生成质量评估，以优化水处理。航空图像用于训练神经网络，以便从这些图像中提取适当的特征，例如草皮草的强度，颜色和纹理。神经网络是机器学习中常用的非线性分类器。神经网络训练模型的输出是草的等级，将其与视觉等级进行比较。目前，对草皮草的质量和颜色（以草的绿色度进行测量）进行了评估。使用Gabor滤波器和共现矩阵计算纹理。还使用其他分类器（例如支持向量机）和更简单的线性回归模型（例如Ridge回归和LARS回归）。比较每个模型的性能。结果表明，使用机器学习技术评估草皮质量和颜色的潜力令人鼓舞。

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