Computer Vision Techniques for Growth Prediction: A Prisma-Based Systematic Literature Review

Abstract

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).

Supervised Machine Learning Approaches for Predicting Key Pollutants and for the Sustainable Enhancement of Urban Air Quality: A Systematic Review

“…The study followed the PRISMA guidelines [24], and a detailed checklist is provided in Table S1 in the Supplementary Material. PRISMA ensures a structured and transparent approach to comprehensive literature reviews, enhancing rigor and reproducibility.…”

Oil Sector and Sentiment Analysis—A Review

“…The review approach employed is based on the PRISMA (preferred reporting items for systematic reviews and meta-analyses) standards, a widely used method for systematic reviews and meta-analyses. Many works used this approach for reviews in other domains, such as computer sciences and medical research [38][39][40]. The PRISMA approach has various advantages.…”

“…To accomplish the task, a systematic literature review based on the PRISMA (Preferred reporting items for systematic reviews and meta-analyses-PRISMA is an evidence-based set of items for reporting in systematic reviews and meta-analyses. For more information, access www.prisma-statement.org (accessed on 15 April 2023)) standards was performed, since it is an already well-established methodology to perform systematic review (e.g., [37][38][39]) and allow for the replication of the results. Out of 320 search results, 34 studies relating to the analysis of sentiment towards the oil market were extracted from the Scopus and Web of Science databases, both of which encompass a large number of different publishers.…”

Wave Power Prediction Based on Seasonal and Trend Decomposition Using Locally Weighted Scatterplot Smoothing and Dual-Channel Seq2Seq Model

“…This adaptability translates into exceptional performance when dealing with diverse time series data. LSTM has shown remarkable efficacy in various domains, including natural language processing [21], time series analysis [22], and computer vision [23]. Figure 2 illustrates the structure of an LSTM unit at time t. Its formal expression is provided in Table 1.…”