Phenotyping plays a crucial role in parameterizing, calibrating, and evaluating process-based plant models, which can be used to understand and predict crop behavior under various conditions. This, in turn, allows for more informed decision-making in agriculture and provides insights into how crops may respond to changing environmental factors. Nevertheless, several potential disparities between current plant models and phenotyping methods exist, which have the potential to undermine the precision and applicability of these models. These discrepancies encompass differences in data resolution and scale (both spatial and temporal) between what is feasibly gathered during phenotyping efforts and what is required by the models. Additionally, issues related to compatibility between the traits measured during phenotyping campaigns and the parameters required for models are prevalent. Furthermore, the representation of genetic and environmental variability in both models and phenotyping data is often limited, resulting in a gap between these two components.To address these discrepancies, we are developing a new “minimum” plant model. This model has been designed with strong consideration of what data can be measured in phenotyping campaigns across multiple genotypes. While the core model remains straightforward, it has the capability to simulate essential processes to represent fundamental plant physiology. Moreover, it has the potential to be integrated into larger, more comprehensive models or parameterized to suit specific genotypes. This approach aims to bridge the gap between phenotyping and crop models, thus enhancing their effectiveness in addressing the challenges that emerge by utilizing them in broader agricultural applications.

Dynamic process-based plant models are computerized representations of plant growth, development, and productivity that use measurements of environmental and physiological processes as input data to make predictions. However, the use of process-based models in phenotyping programs still faces challenge to parameterization across multiple genotypes, such as (1) the need for extensive and intensive datasets as parameters for running simulations, many of which are obtained using destructive and disruptive sampling, (2) the lack of systematic approaches needed to parameterize across extensive collections of genetically distinct, but often related, individuals that are typical of breeding populations. Remote and proximal sensing are potential alternative sources of data to inform parameters of process-based plant models because they can provide fast and non-destructive estimations of plant biophysical parameters across spatial and temporal scales. Over the years, several approaches have been proposed to leverage sensing for model parameterization, from simple empirical tuning to inverse modeling approaches. Continued inquiry into how best to use remote and proximal sensing data to estimate model parameters is critical to the future scale-out of these models for breeding programs and simulating processes that underlie complex genotype-by-environment interactions. Here, we present a decision flowchart to provide a visual representation of the sources and steps for process-based model parameterization that could be used as a guide for researchers working with remote sensing data and crop modeling across numerous genotypes.