Traditional mango pest control requires manual identification. Now deep learning technology is introduced to quickly andaccurately identify mango pests and?diseases. In this paper,12 diseases and insect pests of mango in Panxi area are taken as the researchobject. One part of the data set is from the public data set MangoLeafBD, and the other part is composed of network pictures obtained bycrawler technology. A total of 6 769 pictures were obtained,of which 4 879 were training sets,1 220 were validation sets,and 670 weretest sets. In order to meet the needs of practical applications,four lightweight deep learning networks with?
different scales,such as MobileNetV3 and MobileViT,were selected. Combined with the transfer learning training strategy,comparative experiments were carried outto compare the parameters,accuracy and recall rate of each model. The experimental results show that the MobileViT model has the besteffect on the classification and recognition of mango diseases and insect pests. The accuracy rate of the model is 96. 31% ,the recall rateis 96. 12% ,and the F1 is 96. 20% ,which are better than that of other models. It is showed that such model has excellent robustness andrecognition performance,which can provide technical reference for mango pest classification and recognition.