This paper describes a method of recognizing green perilla leaves using a deep neural network for harvest support in greenhouse horticulture. We are developing a robot for harvest support, which automates the selection and bundling process. In order to manipulate green perilla leaves correctly, the robot needs to precisely estimate their geometrical parameters such as width, height, and orientation. It also needs to detect leaves with anomalies. Therefore, we develop an image-based leaf recognition method, adopting deep neural network (DNN) techniques. To reduce computation time, we design a network for executing multiple tasks simultaneously, namely, segmentation and classification. We also developed an annotated dataset using conventional image processing techniques. Experimental results show the efficiency and effectiveness of the proposed method.

本文介绍了一种使用深层神经网络识别绿色紫苏叶的方法，用于温室园艺的收获支持。我们正在开发一种用于收割支持的机器人，该机器人可自动执行选择和捆绑过程。为了正确地操纵绿色的紫苏叶，机器人需要精确地估计其几何参数，例如宽度，高度和方向。它还需要检测异常叶片。因此，我们采用深度神经网络（DNN）技术开发了一种基于图像的叶子识别方法。为了减少计算时间，我们设计了一个网络来同时执行多个任务，即分段和分类。我们还使用常规图像处理技术开发了带注释的数据集。