应用深度学习神经网络自动图像分析来评估含有类胡萝卜素的绿藻的生长——对环境、健康和水产养殖的重要性。

IF 1.3 4区 医学 Q4 ENVIRONMENTAL SCIENCES
Monika M Zdeb, Mateusz Walo, Grzegorz Łagód
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引用次数: 0

摘要

使用深度学习和神经网络使我们能够大大加快定量研究,并为分析微观图像提供有用的工具。对选定的藻类红球菌(Haematococcus)和Coelastrum sp.进行的研究证实了使用深度学习神经网络的可行性。混淆矩阵显示了与验证数据集相关的YOLO v8网络生成的错误数量。这表明在检测红球菌时的错误率高于牛乳杆菌。F1分数,作为精度和召回率的调和平均值,Coelastrum sp.类的F1分数明显高于Haematococcus sp.类。机器学习不仅可以应用于单个细胞的检测,还可以应用于各种大小的菌落的检测。本文讨论了实施这些先进方法的技术和实践方面,并强调了它们在水产养殖、食品、医疗、可持续能源和环境部门的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of automatic image analysis using a Deep Learning Neural Network for assessing the growth of green algae containing carotenoids - importance for environment, health and aquaculture.

Using deep learning and neural networks enables us to greatly speed-up quantitative studies and provide a useful tool for analyzing microscopic images. Studies conducted on selected algae Haematococcus and Coelastrum sp. confirm the feasibility of using the deep learning neural network. The confusion matrix demonstrated the numbers of errors generated by the YOLO v8 network in relation to the validation dataset. It indicated a higher number of errors in the detection of Haematococcus than Coleastrum. The F1 score, as the harmonic mean of precision and recall, is significantly higher for the class Coelastrum sp. than for Haematococcus sp. Machine learning can be applied not only to the detection of individual cells, but also to the detection of colonies over a wide range of sizes. This article discussed the technical and practical aspects of implementing these advanced methods and highlighted their importance in the aquaculture, food, medical, sustainable energy, and environmental sectors.

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来源期刊
Annals of Agricultural and Environmental Medicine
Annals of Agricultural and Environmental Medicine 医学-公共卫生、环境卫生与职业卫生
CiteScore
3.00
自引率
5.90%
发文量
58
审稿时长
4-8 weeks
期刊介绍: All papers within the scope indicated by the following sections of the journal may be submitted: Biological agents posing occupational risk in agriculture, forestry, food industry and wood industry and diseases caused by these agents (zoonoses, allergic and immunotoxic diseases). Health effects of chemical pollutants in agricultural areas , including occupational and non-occupational effects of agricultural chemicals (pesticides, fertilizers) and effects of industrial disposal (heavy metals, sulphur, etc.) contaminating the atmosphere, soil and water. Exposure to physical hazards associated with the use of machinery in agriculture and forestry: noise, vibration, dust. Prevention of occupational diseases in agriculture, forestry, food industry and wood industry. Work-related accidents and injuries in agriculture, forestry, food industry and wood industry: incidence, causes, social aspects and prevention. State of the health of rural communities depending on various factors: social factors, accessibility of medical care, etc.
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