# Computer Vision Techniques for Growth Prediction:
## 4 Discussion
### Analysis
We evaluated the Paper Quality by checking whether all the items related to the dataset were not blank, whether all the items related to time were not blank, and Transparency. We have understood that growth prediction is feasible at various levels, such as the cellular level (Tumor cellularity), tissue and organismal level (Plant growth), tissue level (Vessel growth), and organismal level (Bone morphology) in Sections 3.1 and 3.2. To predict changes in deer antler growth, such as the growth and replacement of antlers, we must consider which level of growth prediction would be most effective. Figure 5: Comparison of machine learning image prediction models in three fields. Table (**a**) represents the evaluation of studies focusing on tumors, as cited in the following references [29; 30; 33; 37; 74; 78; 74]. Table (**b**) represents the evaluation of studies focusing on bone, as cited in the following references [43; 66]. Table (**c**) represents the evaluation of studies focusing on plant, as cited in the following references [34; 35; 36; 40; 41; 47; 59]. In the context of predicting changes in deer antler growth, particularly the growth and replacement of antlers, the organismal level (Bone morphology) appears to be the most relevant and effective level of growth prediction. This is because antler growth and replacement involve the development and remodeling of bone structures, which are directly related to the morphology of bones at the organismal level. Therefore, employing techniques used for predicting bone growth and development could potentially provide valuable insights into the changes in deer antler growth, including antler growth and replacement. These methods could be tailored to detect and quantify the changes in antler growth, taking into account the factors such as size, shape, and branching patterns. By integrating these approaches into a predictive model, it may be possible to accurately forecast changes in deer antler growth, thus enhancing our understanding of their growth patterns and contributing to better management and conservation strategies. Through this SLR, we have deepened our consideration of the potential applicability of GANs for predicting changes in deer antler, such as antler growth and replacement. It should be noted here that this study is not based on cytology to predict growth but is rooted in the CV field, where analysis is performed from images captured by an RGB camera. The composition of deer antlers is divided into layers such as trabecular bone, cortical bone, and mesenchyme. While incorporating the growth characteristics of these compositions into a growth prediction model as physical equations is a possible future direction, it may be insufficient to account for the complex factors affecting bone growth in the wild, such as collisions, injuries, habitat, and food intake. In this regard, machine learning techniques that predict growth stages from images may be more effective in handling these hard cases, as they can learn to recognize and adapt to a wide range of real-world conditions and variations. GANs, which learns from real-world hard data, has been successfully employed for growth prediction at the cellular level (Tumor cellularity) [33; 37] and Tissue and organismal level (Plant growth) [47]. Given this success, it is worth considering the potential applicability of GANs for predicting changes in deer antler, such as antler growth and replacement. In the context of growth prediction, we conclude that GANs has potential to generate synthetic images representing different growth stages, making them a potential candidate for predicting changes in deer antler. To assess the applicability of GANs for predicting changes in deer antler growth, several factors should be considered:  
(a) Data availability: A large dataset of deer images at different growth stages, particularly focusing on antler development, is required for training the GANs.