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.

中文翻译：

---

可持续农业中基于TC-MRSN模型的玉米叶部病害识别

由真菌病原菌引起的玉米病害是导致玉米减产的首要因素。然而，在实际复杂的背景场景中，孢子引起的病害，如灰斑病、锈病等，通常表现出传播途径多样、感染初期病斑外观相似、病斑大小不一等特点，给病害防治工作带来了挑战性。认识类似的疾病。针对复杂背景下玉米叶部病害的准确识别，提出一种基于深度学习的纹理-颜色双分支多尺度残差收缩网络（TC-MRSN）模型。为了在采样过程中保留小尺寸病灶的特征信息，设计了纹理特征提取块和纹理颜色双分支块来提取病灶的纹理特征并将其与RGB特征融合。为了减少融合特征中冗余背景噪声的干扰，提出多尺度残差收缩模块来提取不同感受野特征并通过软阈值处理冗余噪声。所提出的模型还部署在手机上，以实现实时数据收集和分析。详细的实验和实际测试结果表明，TC-MRSN在复杂背景数据集和PlantVillage数据集上的平均准确率分别达到94.88%和99.59%，高于现有模型ResNet50、VGG-ICNN、HCA- MFFNet 分别增长 5.2%、2.5% 和 1.8%。