Faculty of Science, Yamagata University, 1-4-12 Kojirakawa, Yamagata 990-8560, Japan.
Elix Inc., Daini Togo Park Building 3F, 8-34 Yonbancho, Chiyoda-ku, Tokyo 102-0081, Japan.
Sensors (Basel). 2021 Oct 21;21(21):6999. doi: 10.3390/s21216999.
Monitoring fruit growth is useful when estimating final yields in advance and predicting optimum harvest times. However, observing fruit all day at the farm via RGB images is not an easy task because the light conditions are constantly changing. In this paper, we present CROP (Central Roundish Object Painter). The method involves image segmentation by deep learning, and the architecture of the neural network is a deeper version of U-Net. CROP identifies different types of central roundish fruit in an RGB image in varied light conditions, and creates a corresponding mask. Counting the mask pixels gives the relative two-dimensional size of the fruit, and in this way, time-series images may provide a non-contact means of automatically monitoring fruit growth. Although our measurement unit is different from the traditional one (length), we believe that shape identification potentially provides more information. Interestingly, CROP can have a more general use, working even for some other roundish objects. For this reason, we hope that CROP and our methodology yield big data to promote scientific advancements in horticultural science and other fields.
监测水果的生长情况有助于提前预估最终产量并预测最佳收获时间。然而，通过 RGB 图像全天在农场观察水果并不是一件容易的事情，因为光线条件在不断变化。在本文中，我们提出了 CROP（中央圆形目标绘制器）。该方法涉及基于深度学习的图像分割，神经网络的架构是 U-Net 的更深版本。CROP 可以在不同的光线条件下识别 RGB 图像中的不同类型的中央圆形水果，并创建相应的蒙版。统计蒙版像素可以得到水果的相对二维尺寸，这样，时间序列图像就可以提供一种非接触式的自动监测水果生长的方法。虽然我们的测量单位与传统的单位（长度）不同，但我们认为形状识别可能提供了更多的信息。有趣的是，CROP 具有更广泛的用途，甚至可以用于识别其他一些圆形物体。因此，我们希望 CROP 和我们的方法产生大数据，以促进园艺科学和其他领域的科学进步。
