Handheld Raman spectroscopy for the early detection of plant diseases: Abutilon mosaic virus infecting Abutilon sp.

2016 ◽  
Vol 8 (17) ◽  
pp. 3450-3457 ◽  
Author(s):  
Sivaprasad Yeturu ◽  
Paul Vargas Jentzsch ◽  
Valerian Ciobotă ◽  
Ricardo Guerrero ◽  
Patricia Garrido ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Early Detection ◽  
Mosaic Virus ◽  
Plant Diseases ◽  
Direct Impact ◽  
Abutilon Mosaic Virus

Plant diseases have a direct impact on the productivity of crops, and therefore the early detection of diseases is crucial.

scholarly journals A Gated Recurrent Units (GRU)-Based Model for Early Detection of Soybean Sudden Death Syndrome through Time-Series Satellite Imagery

2020 ◽  
Vol 12 (21) ◽  
pp. 3621
Author(s):  
Luning Bi ◽  
Guiping Hu ◽  
Muhammad Mohsin Raza ◽  
Yuba Kandel ◽  
Leonor Leandro ◽  
...  
Keyword(s):  
Time Series ◽  
Early Detection ◽  
Sudden Death ◽  
Near Infrared ◽  
Time Series Data ◽  
Sudden Death Syndrome ◽  
Plant Diseases ◽  
Series Data ◽  
Detection Accuracy ◽  
Test Accuracy

In general, early detection and timely management of plant diseases are essential for reducing yield loss. Traditional manual inspection of fields is often time-consuming and laborious. Automated imaging techniques have recently been successfully applied to detect plant diseases. However, these methods mostly focus on the current state of the crop. This paper proposes a gated recurrent unit (GRU)-based model to predict soybean sudden death syndrome (SDS) disease development. To detect SDS at a quadrat level, the proposed method uses satellite images collected from PlanetScope as the training set. The pixel image data include the spectral bands of red, green, blue and near-infrared (NIR). Data collected during the 2016 and 2017 soybean-growing seasons were analyzed. Instead of using individual static imagery, the GRU-based model converts the original imagery into time-series data. SDS predictions were made on different data scenarios and the results were compared with fully connected deep neural network (FCDNN) and XGBoost methods. The overall test accuracy of classifying healthy and diseased quadrates in all methods was above 76%. The test accuracy of the FCDNN and XGBoost were 76.3–85.5% and 80.6–89.2%, respectively, while the test accuracy of the GRU-based model was 82.5–90.4%. The calculation results show that the proposed method can improve the detection accuracy by up to 7% with time-series imagery. Thus, the proposed method has the potential to predict SDS at a future time.

Simultaneous Fingerprint and High-Wavenumber Confocal Raman Spectroscopy Enhances Early Detection of Cervical Precancer In Vivo

2012 ◽  
Vol 84 (14) ◽  
pp. 5913-5919 ◽  
Author(s):  
Shiyamala Duraipandian ◽  
Wei Zheng ◽  
Joseph Ng ◽  
Jeffrey J.H. Low ◽  
A. Ilancheran ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Early Detection ◽  
Confocal Raman Spectroscopy ◽  
Cervical Precancer ◽  
Confocal Raman

scholarly journals Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Plants ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1302 ◽  
Author(s):  
Reem Ibrahim Hasan ◽  
Suhaila Mohd Yusuf ◽  
Laith Alzubaidi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Early Detection ◽  
Feature Fusion ◽  
State Of The Art ◽  
Vital Role ◽  
Considerable Improvement ◽  
Plant Diseases ◽  
Recent Emergence

Deep learning (DL) represents the golden era in the machine learning (ML) domain, and it has gradually become the leading approach in many fields. It is currently playing a vital role in the early detection and classification of plant diseases. The use of ML techniques in this field is viewed as having brought considerable improvement in cultivation productivity sectors, particularly with the recent emergence of DL, which seems to have increased accuracy levels. Recently, many DL architectures have been implemented accompanying visualisation techniques that are essential for determining symptoms and classifying plant diseases. This review investigates and analyses the most recent methods, developed over three years leading up to 2020, for training, augmentation, feature fusion and extraction, recognising and counting crops, and detecting plant diseases, including how these methods can be harnessed to feed deep classifiers and their effects on classifier accuracy.

scholarly journals Status and Epidemiology of Maize Lethal Necrotic Disease in Northern Tanzania

Pathogens ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 4 ◽  
Author(s):  
Fatma Hussein Kiruwa ◽  
Samuel Mutiga ◽  
Joyce Njuguna ◽  
Eunice Machuka ◽  
Senait Senay ◽  
...  
Keyword(s):  
Mosaic Virus ◽  
Plant Diseases ◽  
Yellow Dwarf ◽  
Dwarf Virus ◽  
Maize Dwarf Mosaic Virus ◽  
Rt Pcr ◽  
Chlorotic Mottle Virus ◽  
Maize Chlorotic Mottle Virus ◽  
Chlorotic Mottle ◽  
Yellow Dwarf Virus

Sustainable control of plant diseases requires a good understanding of the epidemiological aspects such as the biology of the causal pathogens. In the current study, we used RT-PCR and Next Generation Sequencing (NGS) to contribute to the characterization of maize lethal necrotic (MLN) viruses and to identify other possible viruses that could represent a future threat in maize production in Tanzania. RT-PCR screening for Maize Chlorotic Mottle Virus (MCMV) detected the virus in the majority (97%) of the samples (n = 223). Analysis of a subset (n = 48) of the samples using NGS-Illumina Miseq detected MCMV and Sugarcane Mosaic Virus (SCMV) at a co-infection of 62%. The analysis further detected Maize streak virus with an 8% incidence in samples where MCMV and SCMV were also detected. In addition, signatures of Maize dwarf mosaic virus, Sorghum mosaic virus, Maize yellow dwarf virus-RMV and Barley yellow dwarf virus were detected with low coverage. Phylogenetic analysis of the viral coat protein showed that isolates of MCMV and SCMV were similar to those previously reported in East Africa and Hebei, China. Besides characterization, we used farmers’ interviews and direct field observations to give insights into MLN status in different agro-ecological zones (AEZs) in Kilimanjaro, Mayara, and Arusha. Through the survey, we showed that the prevalence of MLN differed across regions (P = 0.0012) and villages (P < 0.0001) but not across AEZs (P > 0.05). The study shows changing MLN dynamics in Tanzania and emphasizes the need for regional scientists to utilize farmers’ awareness in managing the disease.

A putative replicative form of the abutilon mosaic virus (gemini group) in a chromatin-like structure

1988 ◽  
Vol 212 (2) ◽  
pp. 252-258 ◽  
Author(s):  
Ahmed M. Abouzid ◽  
Thomas Frischmuth ◽  
Holger Jeske
Keyword(s):  
Mosaic Virus ◽  
Replicative Form ◽  
Abutilon Mosaic Virus

Plant Diseases Detection Using Nanowire as Biosensor Transducer

2013 ◽  
Vol 832 ◽  
pp. 113-117 ◽  
Author(s):  
Shahrul A.B. Ariffin ◽  
Tijjani Adam ◽  
U. Hashim ◽  
S. Faridah Sfaridah ◽  
Ishak Zamri ◽  
...  
Keyword(s):  
Mosaic Virus ◽  
Plant Disease ◽  
Plant Diseases ◽  
Working Environment ◽  
Spot Virus ◽  
Biosensor Application ◽  
Papaya Ring Spot Virus ◽  
Ring Spot

The plant disease such as Cucumber Mosaic Virus (CMV) and Papaya Ring Spot Virus (PRSV) is a most dangerous disease that can decrease productivity and quality of the vegetable and fruit. Besides that, its also can destroy and kill those plant in long term when infected and to tackle this problem at early stages, the nanowire based biosensor application is a most reliable sensor nowadays because of advantages towards detecting biological molecule especially plant diseases.In order to dealing with tiny form of molecules such as virus is very difficult and due to the nanostructure uniqueness such as nanowire, it can be done by undergo formation of nanowire process.Result will be elaborated about how nanowire working environment in order to detecting those virus.

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