In recent years, climate change and other anthropogenic factors have contributed to increased crop blight and harmful insects in South Korea crop fields. The main objective of this research was to develop an integrated method and procedure that can be used by unmanned aerial vehicle (UAV) to derive reliable, cost-effective, timely, and repeatable farm information on agricultural production of the field crop at regional level prior to the harvesting date. An attempt has been made in this study to investigate the role of geo-informatics to discriminate different crops at various levels of classification and monitoring crop growth. This research focuses on the evaluation of spatial and temporal variations in crop phenology at Chungbuk using the UAV image data. Crop canopy spectral data in the growing seasons were measured. UAV imagery combined with Smart Farm Map (SFM) were suggested as promising for use in a national crop monitoring system. The test bed area which located in Cheongju were observed by four bands of UAV mounted sensors. UAV images were acquired 6 times from May 6 to October 15, 2016. The difference of normalized difference vegetation index (NDVI) was analyzed. Results showed that NDVI of UAV were strongly correlated with vegetation vigor and growth. The spatial and temporal NDVI and land use and Land cover (LULC) distribution of the crop field were mapped based on the 4-band combination of UAV imagery. The results of this study, we found that the spatial and temporal variation and correlation with crop phenology, LULC classification, and NDVI relationship. The developed model in this study shows a promising result, which can be useful for forecasting crop vegetation conditions in regional scales. Also, the results suggest that the necessary classification performance can be obtained in most of the phenology at crop growing cases, therefore the analysis could be cost effective. The investment to achieve this seems to be worthwhile.