  We present CROP (Central Roundish Object Painter), which identifies and
paints the object at the center of an RGB image. Primarily CROP works for
roundish fruits in various illumination conditions, but surprisingly, it could
also deal with images of other organic or inorganic materials, or ones by
optical and electron microscopes, although CROP was trained solely by 172
images of fruits. The method involves image segmentation by deep learning, and
the architecture of the neural network is a deeper version of the original
U-Net. This technique could provide us with a means of automatically collecting
statistical data of fruit growth in farms. As an example, we describe our
experiment of processing 510 time series photos automatically to collect the
data on the size and the position of the target fruit. Our trained neural
network CROP and the above automatic programs are available on GitHub with
user-friendly interface programs.
