Abstract: Abstract: Influenced by the ecological environment and other factors, the quality and yield of lotus root have been seriously affected by the occurrence of diseases and insect pests in recent years. With the improvement of living standards and the development of the lotus industry chain, people are looking for green food, high-yield and high-quality products. Nowadays, many farmers and planters are unable to accurately identify the diseases and pests of lotus due to lack of professional knowledge of diseases and insect pests control. There is a shortage of efficient, low-cost and automatic identification technology for the prevention and control of lotus diseases and insect pests. The diagnosis and identification of diseases and insect pests are of great significance for the prevention and control of diseases and insect pests in lotus fields. Over the past few years, deep learning technology has been widely used in the field of plant diseases and insect pests recognition to automatically extract the features of plant diseases and insect pests. In order to achieve an efficient and accurate diagnosis of lotus leaf diseases and insect pests, lotus leaf diseases and insect pests dataset was constructed and preliminary experiments were constructed on AlexNet, VGG-16, ResNet50, ResNeXt50, and DenseNet121 models. The experimental results indicated that DenseNet121 has the best performance on the dataset, lotus leaf diseases and insect pests identification model based on improved DenseNet was improved. Firstly, different methods for dynamic data enhancement were compared in this paper. The results show that resizing and randomly resizing the image is more accurate than directly resizing to the same size. The loss of detail information in part of the image is caused by over-compressing the image size, which affects the model's recognition effect. The accuracy of the model was increased from 81.47% to 85.01% by using the data enhancement method of resize, random resized crop, random horizontal flip and random adjust sharpness. AdaMax optimizer was used to replace Stochastic Gradient Optimization optimizer and the accuracy of DenseNet model has been improved by 3.04 percentage points. The Stem block uses multi-layer small convolution for fast dimensionality reduction and a branch structure to combine convolution and maximum pooling. It improves the ability of the model to extract shallow features at a lower operating cost. The Squeeze and Excitation attention mechanism block and sharpen cosine similarity convolution were introduced in the Denselayer of the Dense Block and the Transition Layer. This method improved the recognition ability of the model to lotus leaf diseases, and verified the effectiveness of sharpen cosine convolution to improve the performance of the model. Transfer learning was performed on the Plantvillage dataset. The accuracy of the improved model is 91.34%, which 9.87 percentage points higher than before improvement and optimization. In order to solve the problem of monitoring diseases and insect pests in lotus fields, the improved model was applied to the identification of lotus field diseases and insect pests in UAV images. The calibration area of lotus leaf was cut and predicted by reasoning, then different masks were generated according to the model prediction results and added to the UAV image to generate a distribution map of lotus field diseases and insect pests. The recognition of lotus field diseases and insect pests in the UAV image was investigated, automatic classification and recognition of leaf spot, viral disease, Spodoptera litura, lotus Sclerotium leaf rot and lotus rhizome rot were realized. It provides a new method for efficient and accurate identification and dynamic monitoring of lotus diseases. It also supplies information supports for variable pesticide application and flight path planning in plant disease prevention and control based on UAV.