Abstract：
In this work, a model for the characterization of microalgae cultures based on artificial neural networks has been developed. The characterization of microalgae cultures is essential to guarantee the quality of the biomass, and the objective of this work is to achieve a simple and fast method to address this issue. Data acquisition was performed using FlowCam, a device capable of capturing images of the cells detected in a culture sample, which are used as inputs by the model. The model can distinguish between 6 different genera of microalgae, having been trained with several species of each genus. It was further complemented with a classification threshold to discard unwanted objects while improving the overall accuracy of the model. The model achieved an accuracy of up to 97.27% when classifying a culture. The results demonstrate the effectiveness of the Deep Learning models for the characterization of microalgae cultures, it being a useful tool for the monitoring of microalgae cultures in large-scale production facilities while providing accurate characterization over a wide range of genera.
摘要：
在这项工作中,已经开发了基于人工神经网络的微藻培养物表征模型。微藻培养物的表征对于保证生物质的质量至关重要，这项工作的目的是实现一种简单快速的方法来解决这个问题。使用FlowCam进行数据采集,能够捕获在培养样品中检测到的细胞的图像的装置，它们被模型用作输入。该模型可以区分6个不同属的微藻，受过每种属的几种训练。进一步补充了分类阈值，以丢弃不需要的对象，同时提高模型的整体准确性。对培养物进行分类时，该模型的准确率高达97.27％。结果证明了深度学习模型对微藻培养物表征的有效性，它是在大规模生产设施中监测微藻培养物的有用工具，同时在广泛的属中提供准确的表征。
