  The complex background in the soil image collected in the field natural
environment will affect the subsequent soil image recognition based on machine
vision. Segmenting the soil center area from the soil image can eliminate the
influence of the complex background, which is an important preprocessing work
for subsequent soil image recognition. For the first time, the deep learning
method was applied to soil image segmentation, and the Mask R-CNN model was
selected to complete the positioning and segmentation of soil images. Construct
a soil image dataset based on the collected soil images, use the EISeg
annotation tool to mark the soil area as soil, and save the annotation
information; train the Mask R-CNN soil image instance segmentation model. The
trained model can obtain accurate segmentation results for soil images, and can
show good performance on soil images collected in different environments; the
trained instance segmentation model has a loss value of 0.1999 in the training
set, and the mAP of the validation set segmentation (IoU=0.5) is 0.8804, and it
takes only 0.06s to complete image segmentation based on GPU acceleration,
which can meet the real-time segmentation and detection of soil images in the
field under natural conditions. You can get our code in the Conclusions. The
homepage is https://github.com/YidaMyth.
