Fernández-Campos Mariela, Huang Yu-Ting, Jahanshahi Mohammad R, Wang Tao, Jin Jian, Telenko Darcy E P, Góngora-Canul Carlos, Cruz C D
Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, United States.
Lyles School of Civil Engineering, Purdue University, West Lafayette, IN, United States.
Front Plant Sci. 2021 Jun 17;12:673505. doi: 10.3389/fpls.2021.673505. eCollection 2021.
Wheat blast is a threat to global wheat production, and limited blast-resistant cultivars are available. The current estimations of wheat spike blast severity rely on human assessments, but this technique could have limitations. Reliable visual disease estimations paired with Red Green Blue (RGB) images of wheat spike blast can be used to train deep convolutional neural networks (CNN) for disease severity (DS) classification. Inter-rater agreement analysis was used to measure the reliability of who collected and classified data obtained under controlled conditions. We then trained CNN models to classify wheat spike blast severity. Inter-rater agreement analysis showed high accuracy and low bias before model training. Results showed that the CNN models trained provide a promising approach to classify images in the three wheat blast severity categories. However, the models trained on non-matured and matured spikes images showing the highest precision, recall, and F1 score when classifying the images. The high classification accuracy could serve as a basis to facilitate wheat spike blast phenotyping in the future.
小麦稻瘟病对全球小麦生产构成威胁，且现有的抗稻瘟病品种有限。目前对小麦穗瘟严重程度的评估依赖于人工评估，但这种技术可能存在局限性。将可靠的视觉病害评估与小麦穗瘟的红、绿、蓝（RGB）图像相结合，可用于训练深度卷积神经网络（CNN）进行病害严重程度（DS）分类。通过评分者间一致性分析来衡量在受控条件下收集和分类数据的可靠性。然后，我们训练了CNN模型来对小麦穗瘟严重程度进行分类。评分者间一致性分析表明，在模型训练前具有较高的准确性和较低的偏差。结果表明，所训练的CNN模型为将图像分类到三个小麦稻瘟病严重程度类别提供了一种有前景的方法。然而，在对未成熟和成熟穗图像进行训练的模型在对图像进行分类时显示出最高的精度、召回率和F1分数。高分类准确率可为未来促进小麦穗瘟表型分析提供基础。
