Detection of Bemisia tabaci based on SwinIR super-resolution reconstruction and semantic-SAM model

• Obtained images of Bemisia tabaci on tobacco leaves by using microscopes and smartphones in a cost-effective manner. • The SwinIR model can enhance the image quality and provide more accurate information. • The Semantic-SAM model can effectively segment Bemisia tabaci. • The Feret diameter can identify the different growth stages of Bemisia tabaci . Bemisia tabaci is a common pest on tobacco, which seriously endangers the yield and quality of tobacco leaves. Detection of B. tabaci and accurate calculation of insect population density are the basis for formulating prevention and control strategies. This paper proposes a detection method for whitefly based on SwinIR super-resolution reconstruction and Semantic-SAM model, which can detect and classify B. tabaci with different development stages on tobacco leaves. Firstly, images of B. tabaci on tobacco leaves were collected, and a data set of B. tabaci images was established. Secondly, the SwinIR (Image Restoration Using Swin Transformer) model was used for image super-resolution reconstruction to obtain detailed and clear images. In the B. tabaci identification stage, the Semantic-SAM (Semantic-Segment Anything Model) model was introduced to initially classify B. tabaci ; combined with the feret diameter, the instars of B. tabaci were assisted to distinguish, thus strengthening the classification effect. The experimental results showed that the average recognition accuracy rate of adults was the highest, reaching 100 %; the average recognition rates of the second and third instar nymphs were 86 % and 88 %, respectively; the average recognition rates of eggs, first and fourth instar nymphs were 90 %. The method of combining the SwinIR model, Semantic-SAM model and feret diameter detection proposed in this study can realize the analysis of the development stages and quantity of B. tabaci on tobacco leaves, which is of great significance for the prediction and control of pest populations.