Journal of Agricultural Science and Technology ›› 2023, Vol. 25 ›› Issue (10): 119-125.DOI: 10.13304/j.nykjdb.2022.0218


Image Recognition of Corn Disease Based on Transfer Learning

Yantong ZHANG(), Qianmin SU()   

  1. Institute of Electrical and Electronic Engineering,Shanghai University of Engineering Science,Shanghai 201600,China
  • Received:2022-03-23 Accepted:2023-06-02 Online:2023-10-15 Published:2023-10-27
  • Contact: Qianmin SU


张彦通(), 苏前敏()   

  1. 上海工程技术大学电子电气工程学院,上海 201600
  • 通讯作者: 苏前敏
  • 作者简介:张彦通
  • 基金资助:


The traditional detection of crop disease mainly relies on manpower and experience, and the informatization level is low. In recent years, image recognition based on transfer learning has developed rapidly and achieved good application effect in many fields. MoblieNetV2 model was used to re-train and fine-tune corn disease image data set by transfer learning method. Then, the optimized corn disease recognition model was applied to the mobile terminal device for application development. The results showed that the final test accuracy reached to 96.83% after repeated training and fine-tuning of the pre-training model. Finally, the optimized model was used to develop a corn disease recognition APP, and the corn was photographed through the mobile APP to obtain the diagnosis results. The application was simple and easy to operate, which could facilitate and quickly identify maize diseases and have important application value in the future agricultural field.

Key words: transfer learning, MobileNetV2, image recognition, tensorflow framework, corn disease



关键词: 迁移学习, MobileNetV2, 图像识别, TensorFlow框架, 玉米病害

CLC Number: