The progress made in these areas highlights the potential of deep learning in advancing our understanding and management of plant-related processes. However, deep learning-based algorithms, although often showing superior performance traits related to accuracy (e.g., compared with traditional approaches to computer vision-based tasks, like plant identification), require careful fine-tuning to achieve the highest desired outcomes. At the same time, a very large amount of generic data and available datasets is needed for pre-training and achieving the desired performance of the models [24]. Moreover, their success relies significantly on the availability of an appreciable amount of computing resources that can be utilized to successfully accomplish the assigned tasks. Furthermore, it is quite common for some approaches to utilize plant images during model training that exhibit either optimal imaging acquisition properties or disregard real field conditions. For example, these images might showcase a white or monochrome backdrop encircling the plant, the absence of soil surrounding the plant, or a meticulously prepared monochrome substrate devoid of any fertilizers near the plant. Additionally, these images may lack the presence of moss or depict plants that are not potted, among various other factors. Nevertheless, traditional machine vision, computer vision, and machine learning methods that rely on basic image processing techniques and fundamental machine learning algorithms (e.g., K-means, SVM, KNN, linear regression, random forest, etc. ), especially when targeting smaller image datasets with fewer sensors utilized, still continue to be valuable, preferable, and valid for all the aforementioned factors [25; 26; 27; 28; 29; 30]. Growers and researchers can reap several benefits by integrating machine vision, computer vision, and robotics for plant growth monitoring in greenhouses but also in the field [22; 30; 31; 32]. For example, a constructed machine vision-guided lettuce sensing and features' monitoring system (color, morphological, textual, and spectral) is illustrated in [33]. In [34], an urban robotic farming platform is demonstrated on a laboratory scale for lettuce recognition and subsequent water irrigation tasks. An integrated IoT-based, three-device-system of autonomous moving platforms, aiming to facilitate precision farming and smart irrigation by utilizing the data collected from many sensors, was introduced in [35]. A robotic platform with a vision system and a custom end effector, targeting the harvesting of iceberg lettuce under field conditions, is presented in [36]. In [4], a methodology is presented that combines the application of a digital camera and artificial intelligence for growth prediction as well as the harvest day and growth quality of lettuces in a hydroponic system. An automated growth measurement system for Boston lettuce in a plant factory is developed in [25]. Each lettuce was iteratively photographed, with the cameras moving in predefined increments along the planting beds. Plant growth curves were presented by the authors after applying image processing techniques for each plant's contour detection and the subsequent estimation of each plant's area and height. Similarly, agricultural tasks, like a flower harvesting mechanization, implemented as a three-axis design with mechanical parts based on a computer numerical control machine (CNC) and utilizing YOLOv5 deep learning techniques for the localization and detection of flowers, is presented in [37]. In [14], a small-scale Cartesian robot, aiming at performing functions like seed sowing, spraying, and watering in predefined locations, is presented. The system utilizes YOLO for weed detection. In a similar fashion, in [38], a small farming CNC machine for irrigation, planting, and fertilizer application in predefined locations is implemented.