Arabidopsis thaliana is a model species for uncovering genetic adaptation to alkaline calcareous soils (ACS). This species thrives in ACS, often occurring in dry marginal and urban environments. Existing research largely focused on vegetatively grown seedlings, with a notable lack of studies examining phenotypic variations across the life cycle. A valuable tool for understanding stress resilience is machine-aided phenotyping, as it is non-invasive, rapid, and accurate, but often unavailable to small plant labs. Here, we established and validated an affordable multispectral machine-aided phenotyping approach implementable by individual labs. We collected and correlated quantitative growth data across the entire plant life cycle in response to ACS. We used an A. thaliana wildtype and the coumarin-deficient mutant f6'h1-1, exhibiting chlorosis under alkaline conditions, to assess weekly morphological and leaf color data, both manually and using a multispectral 3D phenotyping scanner. 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 rosette size for growth of A. thaliana in ACS. The most reliable phenotyping was at the beginning of the bolting stage. This methodology is further validated to detect novel leaf chlorosis phenotypes of known iron deficiency mutants across growth stages. Hence, our affordable machine-aided phenotyping procedure is suitable for high-throughput, accurate screening of small-grown rosette plants, including A. thaliana, and enables the discovery of novel genetic and phenotypic variations during the plant's life cycle for understanding plant resilience in challenging soil environments.