The significance of camera-based system growth monitoring lies in its ability to provide non-invasive and dynamic data insights that minimize dependency on manual evaluations, which are time-consuming, labour-intensive, and susceptible to human error. However, existing systematic literature review on this area have failed to provide adequate synthesis based on available evidence. Existing reviews have provided broad views on intelligent and precision agriculture without specific consideration of camera-based plant monitoring systems. In this study, the Preferred Reporting Items for Systematic Reviews. Data from existing studies were retrieved from IEEE Xplore, Science Direct and Scopus databases. From the initial 2271 records that was retrieved, 27 relevant articles were selected to address the formulated research questions. The review explored the state-of-the-art methodologies and technologies used in camera-based plant growth monitoring systems. The corresponding results demonstrates how machine learning and computer vision are applied in the analysis of plant growth and health conditions.