We study the problem of coordinating an Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV) for a precision agriculture application. In this application, the ground and aerial measurements are used for estimating nitrogen (N) levels on-demand across a farm. Our goal is to estimate the N map over a field and classify each point based on N deficiency levels. These estimates in turn guide fertilizer application. Applying the right amount of fertilizer at the right time can drastically reduce fertilizer usage. Towards building such a system, this paper makes the following contributions: First, we present a method to identify points whose probability of being misclassified is above a threshold. Second, we study the problem of maximizing the number of such points visited by an UAV subject to its energy budget. The novelty of our formulation is the capability of the UGV to mule the UAV to deployment points. This allows the system to conserve the short battery life of a typical UAV. Third, we introduce a new path planning problem in which the UGV must take a measurement within a disk centered at each point visited by the UAV. The goal is to minimize the total time spent in traveling and measuring. For both problems, we present constant-factor approximation algorithms. Finally, we demonstrate the utility of our system with simulations which use manually collected soil measurements from the field.