Abstract：
The effective monitoring of microalgae cultivation is crucial for optimizing their energy utilization efficiency. In this paper, a quantitative analysis method, using microalgae images based on two convolutional neural networks, EfficientNet (EFF) and residual network (RES), is proposed. Suspension samples prepared from two types of dried microalgae powders, Rhodophyta (RH) and Spirulina (SP), were used to mimic real microalgae cultivation settings. The method\'s prediction accuracy of the algae concentration ranges from 0.94 to 0.99. RH, with a distinctively pronounced red-green-blue value shift, achieves a higher prediction accuracy than SP. The prediction results of the two algorithms were significantly superior to those of a linear regression. Additionally, RES outperforms EFF in terms of its generalization ability and robustness, which is attributable to its distinct residual block architecture. The RES provides a viable approach for the image-based quantitative analysis.
摘要：
对微藻培养的有效监测对于优化其能量利用效率至关重要。在本文中,定量分析方法，使用基于两个卷积神经网络的微藻图像，效率网络(EFF)和残差网络(RES)，是提议的。由两种干燥的微藻粉末制备的悬浮液样品，红藻（RH）和螺旋藻（SP），用于模拟真实的微藻培养设置。该方法对藻类浓度的预测精度为0.94~0.99。RH,具有明显明显的红-绿-蓝值偏移，实现比SP更高的预测精度。两种算法的预测结果明显优于线性回归。此外,RES在泛化能力和鲁棒性方面优于EFF，这归因于其独特的残差块架构。RES为基于图像的定量分析提供了可行的方法。
