Abstract
In order to count soybean seeds quickly and accurately, improve the
speed of seed test and the level of soybean breeding, a method of
soybean seed counting based on VGG-Two (VGG-T) was developed in this
research. 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 was proposed to speed up the
establishment of publicly available soybean seed image dataset with
annotation. Only 197 min were taken to mark 37,563 seeds when using this
method, which saved 1592 min than ordinary manual marking and could
reduce 96% of manual workload. At the same time, the dataset in this
research is the largest annotated data set for soybean seed counting so
far. Secondly, a method that combined the density estimation-based and
the convolution neural network (CNN) was developed to accurately
estimate the seed count from an individual threshed seed image with a
single perspective. Thereinto, a CNN architecture consisting of two
columns of the same network structure was used to learn the mapping from
the original pixel to the density map. Due to the very limited number of
training samples and the effect of vanishing gradients on deep neural
networks, it is not easy for the network to learn all parameters at the
same time. Inspired by the success of pre-training, this research
pre-trained the CNN in each column by directly mapping the output of the
fourth convolutional layer to the density map. Then these pre-trained
CNNs were used to initialize CNNs in these two columns and fine-tune all
parameters. Finally, the model was tested, and the effectiveness of the
algorithm through three comparative experiments (with and without data
enhancement, VGG16 and VGG-T, multiple sets of test set) was verified,
which respectively provided 0.6 and 0.2 mean absolute error (MAE) in the
original image and patch cases, while mean squared error (MSE) were 0.6
and 0.3. Compared with traditional image morphology operations,
ResNet18, ResNet18-T and VGG16, the method proposed improving the
accuracy of soybean seed counting. In the testset containing soybean
seeds of different densities, the error fluctuation was small, and it
still had excellent counting performance. At the same time, compared
with manual counting and photoelectric seed counter, it saved about
2.493 h and 0.203 h respectively for counting 11,350 soybean seeds,
realizing rapid soybean seeds counting.