Moreover, the utilization of hyperspectral imaging sensors, along with the potential adaptation of other low-cost, yet promising multispectral compact designs [39; 40] suitable for integration into robotic platforms, can be effectively utilized in agricultural applications to perform repetitive activities such as the plant photography that the present work focus on, but also facilitate other interesting agricultural procedures and applications such as fruit harvesting or even fruit ripening assessments. However, a common challenge within the field of computer vision, particularly in the wider scope of agriculture, pertains to the accessibility of datasets for researchers to utilize in order to conduct tests and experiments while developing algorithms or devices. Creating custom image datasets is frequently required, a process that can be quite time consuming, lasting for hours or even days. Additionally, monitoring greenhouse plants and collecting data, especially for research purposes using basic hand-held cameras, can be a laborious, time-consuming, and monotonous undertaking. Moreover, to achieve the necessary accuracy and repeatability during plant image acquisition, the possibility of possessing a robotic mechanism that acts as a reliable and robust recoupment of a human photographer/crop researcher seems to be valuable. With the help of robotic cameras, plant images can be acquired at any time of the day, at the \"exact same spot\", under almost identical conditions (e.g., same viewpoint, distance, and same shooting angle) without the need for a human's presence in the field. The images can then be sent to a remote computer, and the researcher can further systematically monitor plants' health and growth-related traits, with minimal effort. With these factors in mind, the objective of this study was to develop an experimental, low-cost, 1-DOF linear robotic camera system tailored for automated plant photography. As an initial evaluation of the proposed system that targets, as an ongoing project, simplifying the process of plant growth monitoring in a small greenhouse in our university's facilities, this study presents, in detail, the hardware materials used to construct the robotic experimental setup, the software developed for its operation, as well as the methodology followed to assess its construction and movement accuracy along with its ability to accurately identify and localize plants, demonstrated through the examination of lettuce plants primarily in laboratory settings. In this context, the present work focuses on top-view images of the plants, and most image processing and real-time lettuce detection operations are related to the task of color segmentation and the successful analysis of their total projected leaf area and contour, since the top-view images contain more information to monitor plant health than the side-view images [29; 41]. Apart from this introductory section, the rest of the manuscript is organized as follows: Section 2 presents the proposed 1-DOF linear robotic camera system's hardware design and construction in detail along with its software pipeline. Additionally, the experimental setup devised to assess the system's movement accuracy and repeatability is thoroughly explained. Moreover, Section 3 demonstrates the results and the respective discussion over the conducted experiments related to the main aspects of the robotic camera system, and more specifically, its movement's accuracy and repeatability assessment and plant identification and localization ability, while also discussing its limitations; it also provides related work from other research studies and a future research outlook. Finally, Section 4 concludes the manuscript.