Dynamic Remote Sensing Prediction for Wheat Fusarium Head Blight by Combining Host and Habitat Conditions

Abstract

Remote sensing technology provides a feasible option for early prediction for wheat Fusarium head blight (FHB). This study presents a methodology for the dynamic prediction of this classic meteorological crop disease. Host and habitat conditions were comprehensively considered as inputs of the FHB prediction model, and the advantages, accuracy, and generalization ability of the model were evaluated. Firstly, multi-source satellite images were used to predict growth stages and to obtain remote sensing features, then weather features around the predicted stages were extracted. Then, with changes in the inputting features, the severity of FHB was dynamically predicted on February 18, March 6, April 23, and May 9, 2017. Compared to the results obtained by the Logistic model, the prediction with the Relevance Vector Machine performed better, with the overall accuracy on these four dates as 0.71, 0.78, 0.85, and 0.93, and with the area under the receiver operating characteristic curve as 0.66, 0.67, 0.72, and 0.75. Additionally, compared with the prediction with only one factor, the integration of multiple factors was more accurate. The results showed that when the date of the remote sensing features was closer to the heading or flowering stage, the prediction was more accurate, especially in severe areas. Though the habitat conditions were suitable for FHB, the infection can be inhibited when the host’s growth meets certain requirements.

Integrating Remote Sensing and Meteorological Data to Predict Wheat Stripe Rust

“…These results suggest that combining vegetation indices and meteorological features for stripe rust prediction can improve prediction model accuracy. These results are consistent with the findings of Xiao et al and Ma et al in the prediction of wheat scab and wheat powdery mildew [18,71]. In addition, the overall accuracy and kappa coefficient of the RF prediction model constructed using PIVIs outperformed those constructed using VIs by 7.3% and 0.144, respectively.…”

“…The daily meteorological data were obtained for June 2018-2021 and December 2020 from the National Meteorological Information Center in 42 national benchmark weather stations in Sichuan province, Gansu province and Chongqing city adjacent to Qishan county. The inverse distance weighting method in the ArcGIS software was used to spatially interpolate the meteorological data for subsequent continuous spatial pixel-scale analysis [18]. Leave-one-out cross-validation was applied to verify the interpolation accuracy.…”

“…For example, Zhang et al used a series of vegetation indices and meteorological features extracted from HJ remote sensing images and meteorological data, combined with a logistic method to construct a wheat powdery mildew prediction model [17]. Xiao et al successfully constructed a prediction model for wheat scab based on the vegetation indices and meteorological characteristics using Landsat 8 remote sensing images and meteorological data [18]. These studies show that, compared with prediction models based on a single type of feature, the input combination of vegetation indices and meteorological features can effectively improve the accuracy of the prediction model [19].…”

“…These changes lead to a corresponding change in the vegetation indices, which characterizes wheat growth conditions [22,23]. However, the majority of existing studies used remote sensing images of a single date combined with meteorological data to predict diseases, ignoring the impact of phenological differences on crop disease prediction [5,17,18].…”

Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision

“…The performance of the diagnosis model was then improved using a particle swarm optimization support vector machine (PSO-SVM) [27]. Relevance vector machine (RVM) performed better than the logistic model for prediction of FHB severity under natural environmental conditions [28]. However, more advanced algorithms such as convolutional neural network (CNN) have not been adopted in their study.…”