scholarly journals Classifying Wheat Hyperspectral Pixels of Healthy Heads and Fusarium Head Blight Disease Using a Deep Neural Network in the Wild Field

2018 ◽  
Vol 10 (3) ◽  
pp. 395 ◽  
Author(s):  
Xiu Jin ◽  
Lu Jie ◽  
Shuai Wang ◽  
Hai Qi ◽  
Shao Li
Keyword(s):  
Neural Network ◽  
Fusarium Head Blight ◽  
Deep Neural Network ◽  
Head Blight ◽  
In The Wild ◽  
Blight Disease

scholarly journals Auxin as a player in the biocontrol of Fusarium head blight disease of barley and its potential as a disease control agent

2012 ◽  
Vol 12 (1) ◽  
pp. 224 ◽  
Author(s):  
Carloalberto Petti ◽  
Kathrin Reiber ◽  
Shahin S Ali ◽  
Margaret Berney ◽  
Fiona M Doohan
Keyword(s):  
Disease Control ◽  
Fusarium Head Blight ◽  
Control Agent ◽  
Head Blight ◽  
Blight Disease

scholarly journals Identification and Chemotype Profiling of Fusarium Head Blight Disease in Triticale

2021 ◽  
Vol 27 (4) ◽  
pp. 172-179
Author(s):  
Jung-Wook Yang ◽  
Joo-Yeon Kim ◽  
Mi-Rang Lee ◽  
In-Jeong Kang ◽  
Jung- Hyun Jeong ◽  
...  
Keyword(s):  
Sequence Analysis ◽  
Fusarium Head Blight ◽  
Gene Sequence ◽  
Disease Incidence ◽  
Head Blight ◽  
Resistance Response ◽  
Fusarium Asiaticum ◽  
Gene Sequence Analysis ◽  
The Media ◽  
Blight Disease

This study aimed to assess the disease incidence and distribution of toxigenic in Korean triticale. The pathogen of triticale that cause Fusarium head blight were isolated from five different triticale cultivars that cultivated in Suwon Korea at 2021 year. The 72 candidate were classified as a Fusarium asiaticum by morphology analysis and by ITS1, TEF-1α gene sequence analysis. And the results of pathogenicity with 72 isolates on seedling triticale, 71 isolates were showed disease symptom. Also, seven out of 71 Fusarium isolates were inoculated on the wheat, to test the pathogenicity on the different host. The results showed more low pathogenicity on the wheat than triticale. The results of analysis of toxin type with 72 isolates, 64.6% isolates were produced nivalenol type toxin and other 4.6% and 30.8% isolates were produce 3-acetyldeoxynivalenol and 15-acetyldeoxynivalenol, respectively. To select fungicide for control, the 72 Fusarium isolates were cultivated on the media that containing four kinds fungicide. The captan, hexaconazole, and difenoconazole·propiconazole treated Fusarium isolates were not showed resistance response against each fungicide. However, six isolates out of 72 isolates, showed resistance response to fludioxonil. This study is first report that F. asiaticum causes Fusarium head blight disease of triticale in Korea.

scholarly journals Action and reaction of host and pathogen during Fusarium head blight disease

2009 ◽  
Vol 185 (1) ◽  
pp. 54-66 ◽  
Author(s):  
Stephanie Walter ◽  
Paul Nicholson ◽  
Fiona M. Doohan
Keyword(s):  
Fusarium Head Blight ◽  
Head Blight ◽  
Blight Disease

SwingNet

2021 ◽  
Vol 5 (3) ◽  
pp. 1-21
Author(s):  
Hong Jia ◽  
Jiawei Hu ◽  
Wen Hu
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Depth Sensor ◽  
Sports Analytics ◽  
Adversarial Learning ◽  
Depth Sensors ◽  
Network Search ◽  
In The Wild ◽  
Motion Measurements ◽  
Independent Model

Sports analytics in the wild (i.e., ubiquitously) is a thriving industry. Swing tracking is a key feature in sports analytics. Therefore, a centimeter-level tracking resolution solution is required. Recent research has explored deep neural networks for sensor fusion to produce consistent swing-tracking performance. This is achieved by combining the advantages of two sensor modalities (IMUs and depth sensors) for golf swing tracking. Here, the IMUs are not affected by occlusion and can support high sampling rates. Meanwhile, depth sensors produce significantly more accurate motion measurements than those produced by IMUs. Nevertheless, this method can be further improved in terms of accuracy and lacking information for different domains (e.g., subjects, sports, and devices). Unfortunately, designing a deep neural network with good performance is time consuming and labor intensive, which is challenging when a network model is deployed to be used in new settings. To this end, we propose a network based on Neural Architecture Search (NAS), called SwingNet, which is a regression-based automatic generated deep neural network via stochastic neural network search. The proposed network aims to learn the swing tracking feature for better prediction automatically. Furthermore, SwingNet features a domain discriminator by using unsupervised learning and adversarial learning to ensure that it can be adaptive to unobserved domains. We implemented SwingNet prototypes with a smart wristband (IMU) and smartphone (depth sensor), which are ubiquitously available. They enable accurate sports analytics (e.g., coaching, tracking, analysis and assessment) in the wild. Our comprehensive experiment shows that SwingNet achieves less than 10 cm errors of swing tracking with a subject-independent model covering multiple sports (e.g., golf and tennis) and depth sensor hardware, which outperforms state-of-the-art approaches.

scholarly journals Database of resistance related metabolites in Wheat Fusarium head blight Disease (MWFD)

Database ◽  
2017 ◽  
Vol 2017 ◽  
Author(s):  
Anuradha Surendra ◽  
Miroslava Cuperlovic-Culf
Keyword(s):  
Fusarium Head Blight ◽  
Head Blight ◽  
Blight Disease
Sign in / Sign up

Export Citation Format

Share Document