Abstract Fusarium head blight (FHB), caused by the fungus Gibberella zeae, infects spikelets on wheat heads and can cause significant yield and quality losses in wheat. Application of hyperspectral imaging on the detection of FHB was evaluated in the current study. Hyperspectral images were acquired from a total of 1,680 Fusarium-infected wheat head samples over a wavelength range of 400–1000 nm. The principal component analysis was used to reduce dimension of the hyperspectral image. The central wavelengths at 660, 560 and 480 nm were combined into the RGB image and then transferred to YDbDr space. The texture features of the first six principal components were extracted based on gray level co-occurrence matrix and dual-tree complex wavelet transform and the color features extracted in the color space of RGB and YDbDr. Gradient boosting decision tree and sequential backward elimination were applied to select the optimal features, and 50 spectral features and 40 image features were screened. The random forest model was built based on spectral, image, and fusion features of both spectral and image features of wheat heads to determine the optimal features dataset. Then, the deep convolutional neural network (DCNN) was established based on the optimal features dataset. This process resulted in the development of the DCNN model that predicted disease severity most accurately (R2 = 0.97 and RMSE = 3.78). The DCNN model developed from this study can be used as a new tool to detect and predict the FHB disease in wheat.

中文翻译：

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结合光谱和图像数据检测小麦赤霉病

摘要 镰刀菌赤霉病 (FHB) 是由玉米赤霉病菌引起的，它感染小麦头上的小穗，可导致小麦产量和质量的显着损失。本研究评估了高光谱成像在 FHB 检测中的应用。在 400-1000 nm 的波长范围内，从总共 1,680 个受镰刀菌感染的小麦头样品中获取了高光谱图像。主成分分析用于降低高光谱图像的维数。660、560 和 480 nm 的中心波长被组合成 RGB 图像，然后转移到 YDbDr 空间。基于灰度共生矩阵和双树复小波变换提取前6个主成分的纹理特征，在RGB和YDbDr的颜色空间中提取颜色特征。应用梯度提升决策树和顺序向后消除来选择最优特征，筛选出50个光谱特征和40个图像特征。基于麦穗光谱特征和图像特征的光谱特征、图像特征和融合特征建立随机森林模型，确定最优特征数据集。然后，基于最优特征数据集建立深度卷积神经网络（DCNN）。这个过程导致了 DCNN 模型的发展，它最准确地预测了疾病的严重程度（R2 = 0.97 和 RMSE = 3.78）。本研究开发的 DCNN 模型可作为检测和预测小麦 FHB 病害的新工具。基于麦穗光谱特征和图像特征的光谱特征、图像特征和融合特征建立随机森林模型，确定最优特征数据集。然后，基于最优特征数据集建立深度卷积神经网络（DCNN）。这个过程导致了 DCNN 模型的发展，它最准确地预测了疾病的严重程度（R2 = 0.97 和 RMSE = 3.78）。本研究开发的 DCNN 模型可作为检测和预测小麦 FHB 病害的新工具。基于麦穗光谱特征和图像特征的光谱特征、图像特征和融合特征建立随机森林模型，确定最优特征数据集。然后，基于最优特征数据集建立深度卷积神经网络（DCNN）。这个过程导致了 DCNN 模型的发展，它最准确地预测了疾病的严重程度（R2 = 0.97 和 RMSE = 3.78）。本研究开发的 DCNN 模型可作为检测和预测小麦 FHB 病害的新工具。这个过程导致了 DCNN 模型的发展，它最准确地预测了疾病的严重程度（R2 = 0.97 和 RMSE = 3.78）。本研究开发的 DCNN 模型可作为检测和预测小麦 FHB 病害的新工具。这个过程导致了 DCNN 模型的发展，它最准确地预测了疾病的严重程度（R2 = 0.97 和 RMSE = 3.78）。本研究开发的 DCNN 模型可作为检测和预测小麦 FHB 病害的新工具。