基于卷积神经网络的微藻种类识别

微藻在生态系统的结构和功能中具有极为重要的作用，而传统光学人工镜检方法对微藻种类鉴别具有较大的难度。本研究将微藻的光学图像进行了采样，并结合国内外专家对微藻鉴定的经验知识，制作了微藻图像数据集，并进行了数据增强处理。借助深度学习的原理和方法，构建了基于卷积神经网络结构的深度学习模型（AlexNet），对模型进行了训练，并利用5折交叉验证方法确保模型的稳定性。结果表明，模型的训练精度可达到98.78±0.98%，测试精度达85.46±0.23%,达到了预期效果。利用AlexNet模型训练得到的参数，对预留的280个样本图像进行实际测试，7个藻种的平均精确度、平均召回率和平均F1 Score分别为0.832,0.844和0.833。表明深度学习方法是鉴定微藻的一种有效方法。

Identification of microalgae species based on convolutional neural network

In this study, optical images of 7 microalgae were sampled. Based on the experience and knowledge of experts at home and abroad on identification of marine microalgae, an image data set labeled with algae names was made and data enhancement was carried out.With the help of the principles and methods of deep learning, the AlexNet model based on the structure of convolutional neural network was designed and trained.The 5-fold cross validation method was applied to ensure the stability of the model.The results showed that the average training accuracy of the model can reach 97.86%±1.63%and the average testing accuracy can reach 85.86%±0.80%. By using the parameters obtained from AlexNet model training, the reserved 280 sample images were actually tested.The average accuracy, average recall rate and average F1 Score of the 7 algal species were 83.2%,84.4% and 83.3%, respectively.It was indicated that the deep learning method is an effective way to identify marine toxic algal species.