Maize leaf disease recognition based on TC-MRSN model in sustainable agriculture

Maize diseases caused by fungal pathogens are the primary factor resulting in reduced maize yield. However, in practical complex background scenarios, diseases caused by spores, such as gray leaf spot and rust, usually exhibit characteristics including diverse propagation routes, similar lesion appearances at the initial stage of infection, and varying lesion sizes, which raise a challenging task to recognize similar diseases. Focusing on the accurate recognition of maize leaf diseases in complex backgrounds, this paper proposes a texture-color dual-branch multiscale residual shrinkage network (TC-MRSN) model based on deep learning. To preserve the characteristic information of small-sized lesions during the sampling process, texture feature extraction block and texture-color dual-branch block are designed to extract texture features from lesions and fuse them with RGB features. To reduce the interference of redundant background noise in the fusion feature, the multi-scale residual shrinkage module is presented to extract different receptive field features and process redundant noise through soft threshold. The proposed model is also deployed on mobile phones to enable real-time data collection and analysis. Detailed experimental and practical testing results show that TC-MRSN can achieve an average accuracy rate of 94.88% and 99.59% on complex background dataset and PlantVillage dataset, respectively, which is higher than those of the existing models ResNet50, VGG-ICNN, HCA-MFFNet by 5.2%, 2.5% and 1.8%, respectively.