## 4 Conclusions  
The current investigation introduces a new, cost-effective, one-degree-of-freedom linear robotic camera system for automatic plant photography. The system is specifically designed for the automatic and systematic identification and localization of plants ordered in a row, in a complete random design, which can thus can readily facilitate the greenhouse's plant growth monitoring procedures. The entire system was meticulously constructed using readily available materials, parts, and basic electronic components. The primary objective of this study was to offer comprehensive information about the hardware components used, the software pipeline, and the implementation details of the proposed system. A preliminary assessment of the experimental prototype, conducted on a small sample of lettuce plants primarily in laboratory settings, demonstrated encouraging outcomes in terms of plant identification and localization. Although the measurements presented were performed on one type of lettuce plant, we believe that our device can perform just as well with various kinds of plants and in similar applications with minor modifications. Among other things, our device will be useful for creating custom image datasets useful for conducting testing and experimentation, while developing computer vision algorithms. The system was designed and developed by a team of engineers from various disciplines (electronic, computer, and agricultural engineers) to fulfill realistic needs for automatic plant growth monitoring and imaging in an educational greenhouse. The length and height dimensions of the whole construction were chosen so as to align with the crop rows in the greenhouse, ensuring seamless monitoring and imaging processes without disrupting the existing infrastructure. The agricultural engineers, apart from the description of the problem and the initial technical requirements, also closely followed the progress of the implementation, evaluating the intermediate results and intervening with timely observations in the design decisions that were taken. This collaborative approach was continued during the whole development process and extended to both hardware and software development, so a device corresponding to the initial requirements was produced. The same procedure was followed for the evaluation of the construction's operation to monitor the growth of illustrative lettuce samples, feeding back the design and implementation phase with new data that were incorporated into the device. Several such cycles of agile development needed to take place before the device started to operate as required. Such a device is in line with the requirements of Industry 4.0 for smart agriculture, known as Agriculture 4.0, as it can contribute to the automation and modernization of agricultural production. Agriculture 4.0 refers to the anticipated radical changes in agricultural production through the application of recent advances in information technology. Agriculture 4.0 is a term for the next big trends to impact this industry, including a greater focus on precision agriculture with the regular application of the Internet of Things, autonomous robots, big-data, and artificial intelligence to drive greater business efficiencies, make smarter planning decisions, and lower the environmental footprint. Labor-intensive tasks will be undertaken by autonomous ground robots or unmanned aerial vehicles equipped with machine vision systems and precision telemetry. With Agriculture 4.0, the whole agri-food chain will become more competitive and profitable, as deeper insights and precise advice for plant requirements will empower farmers to minimize agricultural input (water, nutrients, plant protection, etc. ), thus minimizing environmental and health hazards, but also to scale production further by reducing space requirements, growing periods, and logistic chains [62].