Automated machine systems play a critical role in plant biology by enabling precise and continuous monitoring of growth and physiological stress under varying environmental conditions. This study presents a systematic evaluation of previously developed machine architectures integrating mechanical positioning units, optical and environmental sensors, and automated data acquisition platforms for high-throughput plant phenotyping. Configurations for measuring morphological traits and physiological indicators, such as leaf area, chlorophyll concentration, and water status, are critically analyzed based on insights from published studies. The performance of these systems is assessed in terms of measurement accuracy, repeatability, adaptability to dynamic plant growth, and sensitivity to environmental fluctuations. Key challenges related to mechanical precision, sensor calibration, and long-term operational stability are discussed. While existing systems provide valuable understanding of plant responses, limitations remain in scalability, adaptability across species, and integration under variable environmental conditions. Based on this evaluation, original perspectives are proposed for next-generation automated monitoring platforms, emphasizing modular mechanical design, adaptive sensor fusion, and intelligent data-processing algorithms. Integration of real-time feedback and machine-learning-based anomaly detection is highlighted as a promising approach for early identification of physiological stress and optimization of growth conditions. This work highlights the significance of engineering-driven machine solutions in biological monitoring and provides a conceptual framework synthesizing insights from current technological developments, guiding interdisciplinary research at the interface of automation, mechatronic systems, and plant biology.