Photonics and machine learning: a powerful partnership for sensing

Abstract

Machine learning is a critical tool for sensing due to its ability to process and interpret complex sensor data, as well as to enhance the accuracy and efficiency of sensing applications in diverse fields. This paper provides an overview of machine learning's multifaceted applications in microwave photonics, soft robotics, and precision agriculture sensing. Recently, machine learning techniques have revolutionized the field of microwave photonics. As an example, we will discuss an implementation of deep learning and generative adversarial network for data argumentation in instantaneous frequency measurement, which effectively decreases required training experimental dataset size by 98.75% and reduces error to <5%. Enhancing the practicability and accuracy of the system. Next, we shift our focus to the integration of fiber optic sensors in soft robotics to offer a lightweight, compact, and soft means of analyzing important robot parameters. By utilizing sensor data, machine learning algorithms enable real-time feedback, adaptability, and improved control of soft robot. Lastly, we also developed fiber optic sensors for non-invasive and continuous underground monitoring of root growth. Monitoring plant root growth is essential for agriculture; however, strain generated by the growth of root is relatively weak and noisy. Therefore, data collected by these fiber sensors is fed to a residual neural network to facilitate extraction of meaningful insights. In summary, machine learning has driven substantial progress in various fields that elevates the levels of accuracy and efficiency beyond previous achievements.