Alkaline calcareous soils (ACS) are prevalent globally and challenge plant growth by limiting nutrient uptake, such as iron. The model plant Arabidopsis thaliana thrives in disturbed urban environments wherein ACS conditions frequently occur. Existing research largely focused on vegetatively grown A. thaliana, while there is a notable lack of studies examining phenotypic variations across the life cycle in ACS. A valuable tool for understanding plant stress resilience is machine-aided phenotyping as it is non-invasive, rapid and accurate. But it is often unavailable to individual plant labs. Here, we established and validated an affordable MicroScan with PlantEye-based machine-aided phenotyping approach, collected and correlated quantitative growth data across plant life cycles in response to ACS. We used A. thaliana wild type and the chlorotic coumarin-deficient mutant f6h1-1 to assess weekly morphological and leaf color data both manually and using a multispectral PlantEye device. Through correlation analysis, we selected machine parameters to differentiate size and leaf chlorosis phenotypes. The correlation analysis indicated a close connection between rosette size and multiple spectral parameters, highlighting the importance of the rosette size for plant growth. Most reliable phenotyping was at the beginning bolting stage. This methodology further is validated to detect novel leaf chlorosis phenotypes of known iron deficiency mutants across growth stages. This affordable machine-aided phenotyping procedure is suitable for high-throughput accurate screening of small-grown rosette plants, such as A. thaliana, and enables the discovery of novel genetic and phenotypic variation during the life cycle for understanding plant resilience in challenging soil environments.