| Zucchini is a common table food in China,because of its rich nutrition,favored by consumers,the planting area increased year by year.The result rate of zucchini decreased and the quality of zucchini deteriorated.Traditional methods of identifying diseases and insect pests of zucchini mainly rely on artificial observation,which leads to low accuracy due to individual differences.In view of this,this paper aims to develop the pest and disease identification model of zucchini in the actual agricultural environment,so as to effectively improve the agricultural practitioners’ inability to timely identify the pest and disease due to the lack of relevant professional knowledge of zucchini.The main innovations of this paper are as follows:1.Build the data set of zucchini diseases and pests.In view of the lack of training data set,1636 samples containing 5 types of diseases,pests and healthy leaves were collected and marked in this paper.2.Transfer learning is introduced to obtain the pre-training model,reduce the over-fitting caused by insufficient data set,and make the model converge faster.3.A new data enhancement method is proposed,which combines traditional probability theory with mathematical statistics,combinatorial mathematics and image processing technology to enrich the pest data set under the premise of fidelity.4.The deep convolutional neural network of Non-local neurons based on the Squeeze-and-Excitation module is proposed for the identification of diseases and insect pests of zucchini,which can fully learn the depth characteristics of samples.In a word,through the above research experiments,a deep convolutional neural network model was designed for the identification of zucchini diseases and insect pests in the actual environment.The experimental results showed that the model had high accuracy and provided important technical support for the research and prevention of zucchini diseases and insect pests. |
