Aerial crop imagery is a beneficial source in improving and managing crop production. Aerial views of crops can help assess the damage done to the crops due to diseases, pests and natural disasters. These images are captured with the help of drones or satellites. This paper presents an algorithm that uses the corps’ colour and texture features to detect damage in the aerial images of the crops. The proposed algorithm first identifies the crop area in the image and the texture features, and then detects the crop pattern’s difference, thereby identifying the damage in the overall crop. The colour features extracted are based on the shifting average mean of pixel values. The texture features extracted include the weighted local entropy, local standard deviation and local pixel range. The colour features help identify the crop area, whereas the texture features distinguish between the normal and damaged crop regions. A combination of all these features resulted in better damage identification.