## 3 Results and Discussion
### Limitations, Future Outlooks, and Related Studies  
Although the proposed system in this work has been shown to be accurate and efficient for its dedicated task tailored to plant photography for research purposes, there are still limitations that we need to consider. One limitation is that the images can only be acquired from the top view, indicating that the error may increase in the scenario where the foliage of neighboring plants partially overlaps. Even though there was no specific requirement for such an arrangement, a common assumption scenario observed in many growth monitoring and phenotyping analysis systems is the spacing of planting beds or the arrangement of pots in a photographical scene, ensuring sufficient distance between them, as, for example, in [4; 25]. In other words, and specifically for our scenario, the five (or six) pots in each row were randomly positioned with sufficient spacing to prevent any overlap of foliage, even when the plants reached their full growth. However, future research endeavors will encompass cases where mature plants exhibit partially overlapping foliage, as the adjacent leaves' segmentation control presents a promising and challenging aspect for the successful identification of plants in a general scenario [3]. Furthermore, future studies will organize dedicated and expanded experiments on a broader field of research to further test our system and to continue to collect images captured in its permanent installation place, that is, the greenhouse. One further limitation is the existing system's ability to recognize and manage only a single plant at a time in a sequential manner, rather than processing multiple plants simultaneously, as demonstrated in [6; 16; 17; 18], for example. Nevertheless, despite its usefulness and potential time-saving benefits, no such limitation existed for the detection of numerous plants and time-related concerns during our design phase. However, since to simultaneously manage multiple plants seems a necessary augmentation, we are currently implementing an algorithmic pipeline towards lettuce plant detection and localization on a panorama image, which is created by combing multiple overlapping images together (image stitching), captured from our system. The results obtained from the conducted experiments clearly indicate that our linear robotic camera is capable of effectively identifying mature plants. Nevertheless, there is room for improvement, which could be achieved by incorporating a higher resolution binocular field stereo system. This parallel adaptation would greatly facilitate the investigation procedures and enable the detection of even the most intricate contours within the projected leaf area, including those that are exceptionally small. The selection of the current stereo camera module was based on its synchronization capabilities, as well as its similarities in characteristics and features to both RGB and IR cameras, as mentioned earlier. In this research, the calibration of the stereo system was extensively discussed, and the IR images were efficiently captured, stored in dedicated folders, and synchronized along with the colored images and datalogging information. However, the primary focus was on the task of plant recognition and localization, which was accomplished using the RGB camera and simple image processing techniques. Consequently, both images representing the same scene are readily available and can be further processed using image registration techniques to derive the relevant Normalized Difference Vegetation Index (NDVI) of the plants, which provides valuable information about their health status. Nevertheless, the calculations associated with the NDVI are reserved for future endeavors. Moreover, as an ongoing project, our experimental plans include the incorporation of different imaging modalities, namely hyperspectral imaging in parallel investigations of different yet interesting image acquisition angle views, other than the top- or side-view imaging modes [41].