# Computer Vision Techniques for Growth Prediction:
## 5 Conclusions and Future Directions  
In this study, we conducted a comprehensive SLR based on PRISMA guidelines for 47 articles related to growth predictions ranging from 2017 to 2022. Our objective of this study was to find out the research trend directly related to growth prediction techniques and algorithms. Our survey followed a systematic step prescribed by PRISMA guideline so that our conclusion is valid and reliable for future research. In this SLR, we extracted more than 526 articles in total from four electronic databases (i.e., IEEE Xplore, ACM Digital Library, Science Direct, and Web of Science) by using the search string introduced in Table 3. We found a few duplicates initially and thus excluded them from our SLR, while 438 articles were excluded based on title and abstract criteria. Articles having segmentation in their keywords and titles were also excluded, as we wanted to focus on growth prediction rather than segmentation. Additionally, articles that included the words: \"crack\", \"fatigue\", \"urban\", \"crystal\", and words related to metal were also removed. We set the inclusion and exclusion criteria to ensure that our conclusion is reliable. After these exclusions, the full texts of 88 studies were comprehensively analyzed and divided into machine-learning and non-machine learning models, with a focus on the machine learning models that appeared most frequently.  
ML has made considerable progress in various fields, including zoology, agriculture, and medical science. We argue that while current growth prediction algorithms using image processing are primarily applied in the medical field. However, we noted that the challenge is still pertained to the field of individual animal recognition. The efficacy and reliability of ML-based individual recognition techniques can be improved by incorporating a variety of image types and employing MT. On the basis of this SLR, we concluded that it is possible to forecast growth not only by GAN but also by various other methods and to apply them to MT. This could lead to more reliable and effective results, especially considering changes in physical characteristics.  
In conclusion, this SLR served as a showcase for scholars who wish to investigate the field of growth prediction using ML technologies. Our results highlighted the studies on the growth predictions particularly focusing on the organs of horns, tails, and other organs of wild animals. This study revealed a sizable vacuum in the literature on these particular characteristics and emphasized the potential of new research directions in this area. Researchers can better grasp these particular animal features' genesis and evolution by digging deeper into their growth patterns. Furthermore, this study may shed light on the variables that affect the conservation efforts of wild animals, which could significantly impact conservation strategy.  
We believe that this SLR makes a significant contribution to the field of growth prediction research, and we hope that it will encourage others to caliber their research in this emerging topic. Additionally, the results of this SLR serve as a significant starting point for the creation of novel growth prediction techniques and algorithms, opening the door to more precise and trustworthy forecasts in various fields, including but not limited to medicine, agriculture, and wildlife conservation. Overall, this SLR paves the way for growth prediction research, and we are forward to report additional findings in our following articles.  
This work was supported by JSPS KAKENHI Grant Number JP23K05416.  
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The authors declare no conflict of interest.