We develop a fast-deployed crop health monitoring system using state-of-the-art technologies to collect data from crop fields in order to take appropriate and timely actions. For the proposed resource optimized system, Saudi Arabian agriculture is taken as a case study. To achieve the desire goals, we harness IoT (Internet of Things) and drones in agriculture to establish rapid system deployment. This paper focuses on the data collection from crop field by organizing heterogeneous IoT devices in clusters and localise them for data harvesting. Clusters are formed by considering the path of UAVs, sensors heterogeneity, weather conditions, fluctuation of sensor nodes, and the communication cost of IoT devices. For localisation, carrying larger or heavier arrays of antennas and receivers with a small size UAV is also a major issue considered in this paper. Hence, we introduce a dynamic clustering and virtual antenna array to develop a complete data collection scheme supported by simulations and experimental tests with proof-of-concept devices. The results are analysed and found promising in terms of energy efficiency, throughput, ease of use, and deployment time. Whole the system is developed with the concept that it can install in rural and remote area with minimum deployment time and agile enough that can collect data in worst conditions (bad weather, hostile environment, fluctuating nodes, poor infrastructure, with or without an established network). In broader sense it can map easily in many similar applications where data is needed to be harvested from a wide range of heterogeneous sensors without existing any infrastructure and ground topology.