Monitoring yellow rust progression during spring critical wheat growth periods using multi-temporal Sentinel-2 imagery.

BACKGROUND: Yellow rust (Puccinia striiformis f. sp. tritici) is a devastating hazard to wheat production, which poses a serious threat to yield and food security in the main wheat-producing areas in eastern China. It is necessary to monitor yellow rust progression during spring critical wheat growth periods to support its prediction by providing timely calibrations for disease prediction models and timely green prevention and control. RESULTS: Three Sentinel-2 images for the disease during the three wheat growth periods (jointing, heading, and filling) were acquired. Spectral, texture, and color features were all extracted for each growth period disease. Then three period-specific feature sets were obtained. Given the differences in field disease epidemic status in the three periods, three period-targeted monitoring models were established to map yellow rust damage progression in spring and track its spatiotemporal change. The models' performance was then validated based on the disease field truth data during the three periods (87 for the jointing period, 183 for the heading period, and 155 for the filling period). The validation results revealed that the representation of the wheat yellow rust damage progression based on our monitoring model group was realistic and credible. The overall accuracy of the healthy and diseased pixel classification monitoring model at the jointing period reached 87.4%, and the coefficient of determination (R2) of the disease index regression monitoring models at the heading and filling periods was 0.77 (heading period) and 0.76 (filling period). The model-group-result-based spatiotemporal change detection of the yellow rust progression across the entire study area revealed that the area proportions conforming to the expected disease spatiotemporal development pattern during the jointing-to-heading period and the heading-to-filling period reached 98.2% and 84.4% respectively. CONCLUSIONS: Our jointing, heading, and filling period-targeted monitoring model group overcomes the limitations of most existing monitoring models only based on single-phase remote sensing information. It performs well in revealing the wheat yellow rust spatiotemporal epidemic in spring, can timely update disease trends to optimize disease management, and provide a basis for disease prediction to timely correct model. © 2024 Society of Chemical Industry.

Migration risk of fall armyworm (Spodoptera frugiperda) from North Africa to Southern Europe.

With the development of globalization and agriculture trade, as well as its own strong migratory capacity, fall armyworm (FAW) (Spodoptera frugiperda) (J.E. Smith) has invaded more than 70 countries, posing a serious threat to the production of major crops in these areas. FAW has now also been detected in Egypt in North Africa, putting Europe, which is separated from it only by the Mediterranean Sea, at high risk of invasion. Therefore, this study integrated multiple factors of insect source, host plant, and environment to provide a risk analysis of the potential trajectories and time periods of migration of FAW into Europe in 2016~2022. First, the CLIMEX model was used to predict the annual and seasonal suitable distribution of FAW. The HYSPLIT numerical trajectory model was then used to simulate the possibility of the FAW invasion of Europe through wind-driven dispersal. The results showed that the risk of FAW invasion between years was highly consistent (P<0.001). Coastal areas were most suitable for the expansion of the FAW, and Spain and Italy had the highest risk of invasion, with 39.08% and 32.20% of effective landing points respectively. Dynamic migration prediction based on spatio-temporal data can enable early warning of FAW, which is important for joint multinational pest management and crop protection.

Detection of wheat Fusarium head blight using UAV-based spectral and image feature fusion.

Infection caused by Fusarium head blight (FHB) has severely damaged the quality and yield of wheat in China and threatened the health of humans and livestock. Inaccurate disease detection increases the use cost of pesticide and pollutes farmland, highlighting the need for FHB detection in wheat fields. The combination of spectral and spatial information provided by image analysis facilitates the detection of infection-related damage in crops. In this study, an effective detection method for wheat FHB based on unmanned aerial vehicle (UAV) hyperspectral images was explored by fusing spectral features and image features. Spectral features mainly refer to band features, and image features mainly include texture and color features. Our aim was to explain all aspects of wheat infection through multi-class feature fusion and to find the best FHB detection method for field wheat combining current advanced algorithms. We first evaluated the quality of the two acquired UAV images and eliminated the excessively noisy bands in the images. Then, the spectral features, texture features, and color features in the images were extracted. The random forest (RF) algorithm was used to optimize features, and the importance value of the features determined whether the features were retained. Feature combinations included spectral features, spectral and texture features fusion, and the fusion of spectral, texture, and color features to combine support vector machine, RF, and back propagation neural network in constructing wheat FHB detection models. The results showed that the model based on the fusion of spectral, texture, and color features using the RF algorithm achieved the best performance, with a prediction accuracy of 85%. The method proposed in this study may provide an effective way of FHB detection in field wheat.