The Assessment of Potato Late Blight Disease in Western Amhara Region, Ethiopia

2021 ◽  
Author(s):  
Gebremariam Asaye Emrie ◽  
Merkuz Abera Admassu ◽  
Adane Tesfaye Lema
Keyword(s):  
Late Blight ◽  
Potato Late Blight ◽  
Amhara Region ◽  
Late Blight Disease ◽  
Blight Disease

Promoting international collaboration for potato late blight disease management

2000 ◽  
Vol 11 (5) ◽  
pp. 181-185 ◽  
Author(s):  
K. V. Raman ◽  
Niklaus J. Grünwald ◽  
William E. Fry
Keyword(s):  
Disease Management ◽  
Late Blight ◽  
International Collaboration ◽  
Potato Late Blight ◽  
Late Blight Disease ◽  
Blight Disease

Utility of Hyperspectral Data for Potato Late Blight Disease Detection

2011 ◽  
Vol 39 (2) ◽  
pp. 161-169 ◽  
Author(s):  
Shibendu Shankar Ray ◽  
Namrata Jain ◽  
R. K. Arora ◽  
S. Chavan ◽  
Sushma Panigrahy
Keyword(s):  
Late Blight ◽  
Potato Late Blight ◽  
Hyperspectral Data ◽  
Disease Detection ◽  
Late Blight Disease ◽  
Blight Disease

RESEARCH EXPERIENCE ON POTATO LATE BLIGHT DISEASE MANAGEMENT IN BANGLADESH

2009 ◽  
pp. 175-186
Author(s):  
M. Hossain ◽  
T.K. Dey ◽  
M. Iqbal Hossain ◽  
S.N. Begum ◽  
M.S. Kadian
Keyword(s):  
Disease Management ◽  
Late Blight ◽  
Potato Late Blight ◽  
Research Experience ◽  
Late Blight Disease ◽  
Blight Disease

scholarly journals Spray-induced gene silencing as a potential tool to control potato late blight disease

2021 ◽  
Author(s):  
Pruthvi B. Kalyandurg ◽  
Poorva Sundararajan ◽  
Mukesh Dubey ◽  
Farideh Ghadamgahi ◽  
Muhammad Awais Zahid ◽  
...  
Keyword(s):  
Late Blight ◽  
Gene Silencing ◽  
Target Genes ◽  
Fluorescent Protein ◽  
Potato Late Blight ◽  
Alternative Means ◽  
Late Blight Disease ◽  
Blight Disease ◽  
Green Fluorescent

AbstractPhytophthora infestans causes late blight disease on potato and tomato and is currently controlled by resistant cultivars or intensive fungicide spraying. Here, we investigated an alternative means for late blight control by spraying potato leaves with double-stranded RNAs (dsRNA) that target P. infestans genes that are essential for infection. Through confocal microscopy, we show that the sporangia of P. infestans expressing Green Fluorescent Protein (GFP) can take up in vitro synthesized dsRNAs homologous to GFP directly from their surroundings, including leaves, which leads to the reduced relative expression of GFP. We further demonstrate the potential of spray induced gene silencing (SIGS) in controlling potato late blight disease by targeting developmentally important genes in P.infestans such as guanine-nucleotide binding (G) protein β-subunit (PiGPB1), haustorial membrane protein (PiHmp1), cutinase (PiCut3), and endo-1,3(4)-β-glucanase (PiEndo3). Our results demonstrate that SIGS can be potentially used to mitigate potato late blight; however, the degree of disease control is dependent on the selection of the target genes.

scholarly journals Novel CropdocNet Model for Automated Potato Late Blight Disease Detection from Unmanned Aerial Vehicle-Based Hyperspectral Imagery

2022 ◽  
Vol 14 (2) ◽  
pp. 396
Author(s):  
Yue Shi ◽  
Liangxiu Han ◽  
Anthony Kleerekoper ◽  
Sheng Chang ◽  
Tongle Hu
Keyword(s):  
Deep Learning ◽  
Late Blight ◽  
Hyperspectral Imagery ◽  
Feature Space ◽  
Structural Diversity ◽  
Potato Late Blight ◽  
Disease Diagnosis ◽  
Spatial Features ◽  
Late Blight Disease ◽  
Blight Disease

The accurate and automated diagnosis of potato late blight disease, one of the most destructive potato diseases, is critical for precision agricultural control and management. Recent advances in remote sensing and deep learning offer the opportunity to address this challenge. This study proposes a novel end-to-end deep learning model (CropdocNet) for accurate and automated late blight disease diagnosis from UAV-based hyperspectral imagery. The proposed method considers the potential disease-specific reflectance radiation variance caused by the canopy’s structural diversity and introduces multiple capsule layers to model the part-to-whole relationship between spectral–spatial features and the target classes to represent the rotation invariance of the target classes in the feature space. We evaluate the proposed method with real UAV-based HSI data under controlled and natural field conditions. The effectiveness of the hierarchical features is quantitatively assessed and compared with the existing representative machine learning/deep learning methods on both testing and independent datasets. The experimental results show that the proposed model significantly improves accuracy when considering the hierarchical structure of spectral–spatial features, with average accuracies of 98.09% for the testing dataset and 95.75% for the independent dataset, respectively.

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