Department of Electrical Engineering, Mokpo National University, Muan 58554, Korea.
Components & Materials R&D Group, Korea Institute of Industrial Technology, Gwangju 61012, Korea.
Sensors (Basel). 2022 Jul 8;22(14):5140. doi: 10.3390/s22145140.
Tomato sucker or axillary shoots should be removed to increase the yield and reduce the disease on tomato plants. It is an essential step in the tomato plant care process. It is usually performed manually by farmers. An automated approach can save a lot of time and labor. In the literature review, we see that semantic segmentation is a process of recognizing or classifying each pixel in an image, and it can help machines recognize and localize tomato suckers. This paper proposes a semantic segmentation neural network that can detect tomato suckers quickly by the tomato plant images. We choose RGB-D images which capture not only the visual of objects but also the distance information from objects to the camera. We make a tomato RGB-D image dataset for training and evaluating the proposed neural network. The proposed semantic segmentation neural network can run in real-time at 138.2 frames per second. Its number of parameters is 680, 760, much smaller than other semantic segmentation neural networks. It can correctly detect suckers at 80.2%. It requires low system resources and is suitable for the tomato dataset. We compare it to other popular non-real-time and real-time networks on the accuracy, time of execution, and sucker detection to prove its better performance.
番茄的卷须或腋芽应予以摘除，以提高产量并减少番茄植株的病害。这是番茄植株护理过程中的一个重要步骤。通常由农民手动完成。自动化方法可以节省大量的时间和劳动力。在文献综述中，我们看到语义分割是一种识别或分类图像中每个像素的过程，它可以帮助机器识别和定位番茄的卷须。本文提出了一种语义分割神经网络，可以通过番茄植株的图像快速检测番茄的卷须。我们选择了 RGB-D 图像，它不仅可以捕捉物体的视觉信息，还可以捕捉物体到相机的距离信息。我们制作了一个番茄 RGB-D 图像数据集，用于训练和评估所提出的神经网络。所提出的语义分割神经网络可以在每秒 138.2 帧的速度下实时运行。它的参数数量为 680,760，比其他语义分割神经网络小得多。它可以正确检测到 80.2%的卷须。它需要的系统资源低，适用于番茄数据集。我们将其与其他流行的非实时和实时网络在准确性、执行时间和卷须检测方面进行了比较，以证明其更好的性能。
