This study was aimed at detecting flower areas in Convallaria keiskei colonies by using unmanned‐aerial‐vehicle (UAV) images. To estimate the number of Convallaria keiskei colonies, we focused on the effective features obtained from the information of flower color and location in the image obtained from the UAV. We proposed a method for estimating the number of colonies by combining image processing and machine learning based on the characteristics of Convallaria keiskei flowers. To evaluate the new effect of the proposed method, we compared the results obtained from the proposed method with those obtained from the comparison method that uses image processing focusing only on brightness and saturation. The results suggest that the proposed method that combines image processing and machine learning is effective for estimating the number of Convallaria keiskei colonies. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

中文翻译：

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基于卷积神经网络的无人机图像估计铃兰的菌落数量

本研究旨在通过无人飞行器（UAV）图像检测铃兰铃兰菌落的花区。为了估计铃兰铃兰菌落的数量，我们集中于从花色信息和从无人机获得的图像中的位置信息中获得的有效特征。我们基于铃兰花的特征，提出了一种通过结合图像处理和机器学习来估计菌落数量的方法。为了评估所提出方法的新效果，我们将所提出方法的结果与仅使用亮度和饱和度的图像处理方法所获得的结果进行了比较。结果表明，所提出的结合图像处理和机器学习的方法对于估计铃兰铃兰菌落的数量是有效的。©2020日本电气工程师学会。由Wiley Periodicals LLC发布。