Growth prediction technology is not only a practical application but also a crucial approach that strengthens the safety of image processing techniques. By supplementing the growth images obtained from the original images, especially in insufficient data sets, we can increase the robustness of machine learning. Therefore, predicting the growth of living organisms is an important technology that increases the safety of existing applications that target living organisms and can extend to areas not yet realized. This paper is a systematic literature review (SLR) investigating biological growth prediction based on the PRISMA 2020 guidelines. We systematically survey existing studies from 2017 to 2022 to provide other researchers with current trends. We searched four digital libraries—IEEE Xplore, ACM Digital Library, Science Direct, and Web of Science—and finally analyzed 47 articles. We summarize the methods used, year, features, accuracy, and dataset of each paper. In particular, we explained LSTM, GAN, and STN, the most frequently used methods among the 20 papers related to machine learning (40% of all papers).

In Fizeau interferometry for high-numerical-aperture spherical surface tests, the mechanical phase shift becomes spatially nonuniform within the observation aperture. We divided the aperture into annular regions and calculated the object phase using several algorithms designed for different phase shifts. The division substantially decreased the nonuniformity; however, it caused bias errors at the regional boundaries in the measured phase. The error is due to the different error coefficients of the algorithms for the phase-shift nonlinearity. A convolution technique that modifies a sampling window to align the error coefficients of a set of algorithms is proposed. The technique is experimentally shown to minimize phase measurement errors.