Wheat is an important source of dietary fiber and protein that is negatively impacted by a number of risks to its growth. The difficulty of identifying and classifying wheat diseases is discussed with an emphasis on wheat loose smut, leaf rust, and crown and root rot. Addressing conditions like crown and root rot, this study introduces an innovative approach that integrates multi-scale feature extraction with advanced image segmentation techniques to enhance classification accuracy. The proposed method uses neural network models Xception, Inception V3, and ResNet 50 to train on a large wheat disease classification dataset 2020 in conjunction with an ensemble of machine vision classifiers, including voting and stacking. The study shows that the suggested methodology has a superior accuracy of 99.75% in the classification of wheat diseases when compared to current state-of-the-art approaches. A deep learning ensemble model Xception showed the highest accuracy.

翻译：小麦是膳食纤维和蛋白质的重要来源，其生长过程受到多种风险因素的负面影响。本文探讨了小麦病害识别与分类的难点，重点关注小麦散黑穗病、叶锈病以及冠腐病和根腐病。针对冠腐病和根腐病等病害，本研究提出了一种创新方法，将多尺度特征提取与先进的图像分割技术相结合，以提高分类准确率。该方法采用神经网络模型Xception、Inception V3和ResNet 50，在大型小麦病害分类数据集2020上进行训练，并结合投票法和堆叠法等机器视觉分类器集成策略。研究表明，与当前最先进的方法相比，所提出的方法在小麦病害分类中达到了99.75%的优异准确率。其中集成深度学习模型Xception表现出最高的分类精度。