ECA-SKNet:玉米单倍体种子的卷积神经网络识别模型

ECA-SKNet:Convolutional Neural Network Identification Model for Corn Haploid Seeds

采用3000张玉米种子图像进行基于卷积神经网络的玉米单倍体种子识别,包含1230张单倍体玉米种子图像和1770张二倍体玉米种子图像。为对比不同卷积神经网络模型在单倍体玉米种子识别的效果,使用VGG、ResNet、DenseNet和SKNet等经典模型,并对SKNet模型进行改进,将其降维升维全连接层设计为一维卷积以降低模型参数数量,改进后的SKNet称为ECA_SKNet。对5种模型使用相同优化器和训练周期进行实验,结果表明:实验模型均能对单倍体玉米种子达到较好的识别效果,最低准确率能达88.5%,ECA_SKNet模型准确率达93.04%。可见,卷积神经网络在玉米单倍体种子识别中能够发挥重要作用,为作物种子识别提供新思路。

In this paper,a study is conducted on corn haploid seeds recognition based on convolutional neural network using 3000 corn seed images with 1230 haploid corn seed images and 1770 diploid corn seed images.In order to compare the effect of different convolutional neural network models on haploid corn seeds recognition,classical models including VGG,ResNet,DenseNet and SKNet are adopted,and the SKNet model is improved by replacing the fully-connected layer in dimensionality reduction and dimensionality increase with one-dimensional convolution to further reduce the number of model parameters,and the improved SKNet is called ECA_SKNet.The experimental results show that aforementioned five models can achieve good recognition of haploid corn seeds with the lowest accuracy of 88.5%and the accuracy of ECA_SKNet can reach 93.04%.It is seen that convolutional neural networks can play an important role for the recognition of corn haploid seeds and provide a new way to recognize crop seeds.