Affiliations: [a] Institute of Data Science and Agricultural Economy, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China | [b] Beijing Research Center of Engineering Technology on Rural Distance Information Service, Beijing, China | [c] CRRC Group Co., Ltd, Hunan, China | [d] Landscape Bureau of Xinzhou District, Wuhan, Hubei, China
Correspondence:
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Corresponding author: Changshou Luo, Institute of Data Science and Agricultural Economy, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China. E-mail: [email protected].
Abstract: In order to solve the problem of long training time and large samples required by traditional image recognition model, a method of crop pest recognition based on transfer learning and data conversion was proposed. It takes CNN models such as Inception V3, VGG16, ResNet as the backbone structure. And the transfer learning was used to improve the model effect. The original picture data was expanded through the transformation of flip, rotation, scale, crop, translation and shading. Based on the data of 11 common pests such as white grub, east asian locust and whitefly etc., the model training and recognition was carried out. The result shows that, the accuracy of transfer learning model is higher than that of non-transfer learning model. The Inception V3 model performs well of all, the recognition accuracy is more than 98.94%. Through the analysis of cross entropy and confusion matrix, data transformation is helpful to improve the accuracy of the model with small sample.
Keywords: Transfer learning, data transformation, pest identification, crops
