‘Individualized’ modeing of biophysical entities: Individual plants behave and develop differently due to variable environmental conditions (soil, local nutrient availability, local microclimate, local stress). The use of predictive (temporal) models at the individual plant scale integrated with field-level modeling using a networked, hierarchical approach will allow cyber-physical modeling and control at that plant-scale resolution for large areas. Current capabilities in persistent environmental sensing can providehyper-local environmental conditions, suggesting that individualized models are possible. However, relying only on data to create such models leaves out contextual constraints and valuable domain knowledge. This calls for principled methods for creating individualized plant models that tightly couple multi-scale data with known biophysical and physiological knowledge. This will additionally ensure that model predictions follow known biophysical rules, thus ensuring generalizability.
‘Individualized’ sensing using multi-modal data fusion and robust learning: Updating the individualized models require multi-modal measurements to estimate the state variables. These measurements will be performed at different scales of environmental conditions and plant physiology, and they will potentially be quite noisy due to degraded sensing environments. Hence, robust machine learning approaches are needed for feature extraction and fusion of multi-scale, multi-modal data to update the models.
‘Individualized’ actuation using dexterous robots: Individualized actuation include localized chemical and water distribution (spraying, injection) and mechanical crop management operations. This requires dexterous actuators to enable precise chemical dispersion. Our prior work shows substantial promise in outfitting small, cheap autonomous ground vehicles with dexterous actuators in order to minimize the risk of accidents.