Since manual sorting of apples is a huge project in our daily life, an apple image classification approach based on multi-perspective multi-region feature fusion was proposed. First of all, five classes of apples, containing 329 in total, were collected. For each apple, five images from five different perspectives were obtained:top, bottom, side1, side2 and side3. From each image, several (one to nine) small image regions were cut. Secondly, each region block was represented by color histogram vector, and the histogram vectors of region blocks were fused together end to end to generate a representation of the image. Finally, 12 classifiers were used to classify 329 samples. The experimental results show that the multi-perspective multi-region based method significantly outperforms single-perspective single-region based method, and the more the number of perspectives/regions, the better the result. In particular, classification performance reaches 97.87% by PLS (Partial Least Squares) even better than deep learning when using nine regions for each image cropped at five angles. The method is easy but efficient, whose computation complexity is 4 n, where n is the total number of blocks in image cropping area. Thus, it can be applied to mobile applications and applied to more fruit and plant image classification.