Most of current crop-disease recognition approaches mainly focus on improving the recognition accuracy on public datasets

while ignoring the recognition robustness.When dealing with real-world recognition problem

the actual recognition accuracy of those approach are often unsatisfactory because of noise interference and environmental influence. To address these issues

we propose a high-order residual and parameter-sharing feedback convolutional neural network (HORPSF) for crop-disease recognition. The high-order residual subnetwork is helpful for improving the recognition accuracy of crop disease. The parameter-sharing feedback subnetwork can effectively depress the background noises and enhance the robustness of the model. Extensive experiment results demonstrate that the proposed HORPSF approach significantly outperforms other competing methods in terms of recognition accuracy and robustness

especially demonstrating superior performance when dealing with the real-world examples of crop-disease recognition.