Abstract
In order to count soybean seeds quickly and accurately, improve the
speed of seed test and the level of soybean breeding , this dissertation
developed a method of soybean seed counting based on VGG-Two (VGG-T).
Firstly, in view of the lack of available image dataset in the field of
soybean seed counting, a fast target point labeling method of combining
pre-annotation based on digital image processing technology with manual
correction annotation is proposed to speed up the establishment of
publicly available soybean seed image dataset with annotation. This
method only takes 197 minutes to mark 37,563 seeds, which saves 1,592
minutes than ordinary manual marking and replaces 96% of manual
workload. Secondly, a method that would combine the density
estimation-based methods and the convolution neural network (CNN)-based
methods is developed to accurately estimate the seed count from an
individual threshed seed image with a single perspective. Finally, the
model is tested, and verify the effectiveness of the algorithm through
three comparative experiments (with and without data enhancement, VGG16
and VGG-T, multiple sets of test set), which respectively provided 0.6
and 0.2 mean absolute error (MAE) in the original image and patch cases,
while mean squared error (MSE) is 0.6 and 0.3. Compared with traditional
image image morphology operations, ResNet18, ResNet18-T and vgg16, this
method improves the accuracy of soybean seed counting.