FINANCIAL AND SOCIAL ELEMENTS OF MODERN SOCIETIES ARE CLOSELY CONNECTED TO THE CULTIVATION OF PLANTS LIKE CORN AND SOYBEAN. DUE TO THE MASSIVE IMPORTANCE OF THESE PLANTS, NITROGEN OR POTASSIUM DEFICIENCIES DURING THEIR CULTIVATION PROCESS DIRECTLY TRANSLATE TO MAJOR FINANCIAL LOSSES. THEREFORE, THE EARLY DETECTION AND TREATMENT OF THESE NUTRIENT DEFICIENCIES IS A TASK OF GREAT SIGNIFICANCE AND VALUE. HOWEVER, CURRENT STANDARD FIELD SURVEILLANCE PRACTICES ARE EITHER COMPLETED MANUALLY OR WITH THE ASSISTANCE OF SATELLITE IMAGING, WHICH OFFERS ONLY INFREQUENT, INSUFFICIENT (FROM SPATIAL RESOLUTION PERSPECTIVE), AND COSTLY DATA TO FARMERS. AS A RESULT, FARMERS TEND TO MINIMIZE RISK THROUGH THE APPLICATION OF UNIFORM RATES OF FERTILIZER TO THE FIELD IN FALL PRIOR TO PLANTING IN SPRING (IN THE CASE OF CORN). THIS APPROACH OVERESTIMATES THE AMOUNT OF FERTILIZER NEEDED WHILE PRODUCING MASSIVE NITROGEN CONTAMINATION OF SURFACE AND GROUNDWATER.THIS PROJECT PROMOTES THE USE OF AUTONOMOUS TEAMS OF SMALL AERIAL AND GROUND CO-ROBOTS, ARMED WITH EFFICIENT PLANT-CENTRIC INFORMATION GATHERING ALGORITHMS AND MULTI-MODAL PERCEPTION ABILITIES THAT FUSE INFORMATION FROM THE VISIBLE SPECTRUM (RGB), AS WELL AS MULTI-- OR HYPER--SPECTRAL DOMAINS. IT USES CORN AS THE TARGET CROP. THE OVERARCHING GOAL OF THIS WORK IS TO INTRODUCE AN AUTOMATED STRATEGY FOR PLANT FIELD ROBOTIC MAPPING, MONITORING, NITROGEN, AND POTASSIUM DEFICIENCY DETECTION AND CROP BIOMASS ESTIMATION AT FINE SPATIO-TEMPORAL RESOLUTIONS, IN ORDER TO BETTER ESTIMATE THE NUTRIENT FERTILIZER REQUIREMENTS. THROUGH THE CAPACITY OF THE AERIAL AND GROUND ROBOTIC TEAM TO AUTONOMOUSLY SELECT AND FOLLOW THE VIEWPOINTS THAT ENABLE COMPREHENSIVE MULTI-MODAL 3D RECONSTRUCTION OF THE CORN CANOPY STRUCTURE (BIOMASS) AT ARBITRARY RESOLUTIONS, A SUPERIOR ALTERNATIVE TO HIGH ALTITUDE AERIAL IMAGING IS SUGGESTED.