To solve the time-consuming and laborious manual counting investigation and low recognition rate of larvae in the laboratory. This thesis studies the latest Flask framework and artificial intelligence-based image processing technology to realize a mobile application automatically classifying and counting larvae. In order to realize the counting and classification of larvae images for small data set, larva image segmentation based on grayscale thresholding and edge detection methods and an image recognition model based on transfer learning is proposed. Based on the VGG-16 model, a new fully-connected layer module was designed. The VGG-16 model was migrated to the model in the trained convolution layer of the ImageNet image data set.