Maize Disease Recognition Based On Image Enhancement And OSCRNet

Abstract Background: Under natural light irradiation, there are significant challenges in the identification of maize leaf diseases because of the difficulties in extracting lesion features from constantly changing environments, uneven illumination reflection of the incident light source and many other factors.Results: In the present paper, a novel maize image recognition method was proposed. Firstly, an image enhancement framework of the maize leaf was designed, and a multi-scale image enhancement algorithm with color restoration was established to enhance the characteristics of the maize leaf in a complex environment and to solve the problems of high noise and blur of maize images. Subsequently, an OSCRNet maize leaf recognition network model based on the traditional ResNet backbone architecture was designed. In the OSCRNet maize leaf recognition network model, an octave convolution with characteristics to accelerate network training was adopted, reducing unnecessary redundant spatial information in the maize leaf images. Additionally, a self-calibrated convolution with multi-scale features was employed to realize the interactions of different feature information in the maize leaf images, enhance feature extraction, and solve the problems of similarity of maize disease features and easy learning disorders. Concurrently, batch normalization was employed to prevent network overfitting and enhance the robustness of the model. The experiment was conducted on the maize leaf image data set. The highest identification accuracy of rust, grey leaf disease, northern fusarium wilt, and healthy maize was 94.67%, 92.34%, 89.31% and 96.63%, respectively. Conclusions: The aforementioned methods were beneficial in solving the problems of slow efficiency, low accuracy and image recognition training, and also outperformed other comparison models. The present method demonstrates strong robustness for maize disease images collected in the natural environment, providing a reference for the intelligent diagnosis of other plant leaf diseases.