Abstract:The automatic monitoring system for crops has the advantages of automatic, non-contact and non-destructive, and is a useful supplement to traditional agrometeorological observations. The charge coupled device (CCD) sensor protects the cover from water, dust, fog-haze, rain and snow, etc., which may cause observation data error of automatic crop monitoring system. Therefore, the quality control is the basis for the rational usage of the automatic crop monitoring system. Based on the historical crop image data of Zhengzhou, Tai’an and Gucheng, this paper designs a quality control method for image data based on color feature parameter detection and dark channel prior histogram detection. According to different weather conditions,we carried out quality control on the summer maize and winter wheat image observation data in 2010-2012, and the effects were tested. The results show that both types of inspection methods can determine the anomaly data of image observation data in the automatic crop monitoring system. The color feature parameter detection method can effectively identify the missing image of pixels, and the accuracy rate can reach 100%. The proposed method based on the histogram of dark channel can effectively identify the contaminated image, with the average precision being 95.7% and recall average 87.5%. This quality control method can reduce the error between the model estimate and the observed data. At present, this method has been applied to automatic agricultural meteorology observation oprational software.