Automatic detection of ‘yellow rust’ in wheat using reflectance measurements and neural networks

Moshou, Dimitrios; Bravo, C.; West, Jonathan; Wahlen, Stijn; McCartney, Alastair; Ramón, Herman

doi:10.1016/j.compag.2004.04.003

Cited by 288 publications

(137 citation statements)

References 10 publications

Supporting

Mentioning

133

Contrasting

Unclassified

Order By: Relevance

“…11,12,Tables 6,7). Some previous studies have already emphasized the potential of hyperspectral imagery Moshou et al 2004;Huang et al 2007) and the high-resolution of multispectral imagery (Franke and Menz 2007) for detecting yellow rust disease. The development of SKB in the present study can be viewed as a scaling up method, which has extended the capability of detecting yellow rust disease from hyper-spectral imagery to the moderate resolution of multispectral imagery.…”

Section: Performance Of Skb For Field Surveyed Datamentioning

confidence: 99%

Crop Disease and Pest Monitoring by Remote Sensing

Huang¹,

Luo²,

Zhang³

et al. 2012

Remote Sensing - Applications

View full text Add to dashboard Cite

Section: Performance Of Skb For Field Surveyed Datamentioning

confidence: 99%

Crop Disease and Pest Monitoring by Remote Sensing

Huang¹,

Luo²,

Zhang³

et al. 2012

Remote Sensing - Applications

View full text Add to dashboard Cite

“…Earlier studies have reported the relationships between spectral reflectance and level of rust infection [12][13][14][15]. Normalized difference vegetation index (NDVI) was reported to be useful in predicting photosynthetic activity, plant health and level of stresses, including plant diseases.…”

Section: Data Collectionmentioning

confidence: 99%

Wheat Yield Response to Foliar Fungicide Application against Leaf Rust Caused by Puccinia triticina

Joshi¹,

Ullah²,

Rehman³

et al. 2017

JAST-A

View full text Add to dashboard Cite

Abstract:A study was conducted on reducing the yield loss of wheat due to leaf rust caused by Puccinia triticina with foliar application of fungicides during the 2014-2015 and 2015-2016 growing seasons at the Wheat Research Institute in Faisalabad, Pakistan. Three fungicides: Folicur (tebuconazole) at 300 mL/ha, Nativo (tebuconazole + trifloxystrobin) at 300 g/ha and Tilt (propiconazole) at 500 mL/ha were applied single or two times to Morocco and Sehar-06 wheat varieties used in the trial. The trial plots were first sprayed at the Zadok's scale (ZS) 3 stage and second sprayed between ZS 4.3 and 5.4 stages. The greenness of the trial crop was measured using GreenSeeker. Foliar application of fungicides significantly reduced the loss of grain yield and 1,000-grain weight (TGW) of wheat due to leaf rust in comparison to the control without fungicides application. Of the three fungicides, two times spray of Nativo reduced the grain yield loss of leaf rust susceptible mega wheat variety Sehar-06 by 45%-56% and the loss of TGW by 42%, also giving the highest marginal return in the trial. Single application of Nativo was equally effective as two times spray of Folicur in reducing the loss of wheat grain yield. Two times spray of Folicur was found to be the second choice of fungicide for reducing the yield loss of wheat. The research identified suitable fungicides for reducing the yield loss of wheat due to leaf rust and also generated important scientific knowledge required to manage a sudden outbreak of leaf rust to ensure food security.

show abstract

“…For such complex data analyses, the application of artificial neural networks may provide further improvement of detection results. With this method, [106] could increase detection success of stripe rusts infection on wheat to nearly 99%.…”

Section: Improvement Of Disease Recognition By Sensor Fusionmentioning

confidence: 99%

Hyperspectral and Chlorophyll Fluorescence Imaging for Early Detection of Plant Diseases, with Special Reference to Fusarium spec. Infections on Wheat

2014

View full text Add to dashboard Cite

Abstract:In recent years, market pressures have reinforced the demand to solve the problem of an increased occurrence of Fusarium head blight (FHB) in cereal production, especially in wheat. The symptoms of this disease are clearly detectable by means of image analysis. This technique can therefore be used to map occurrence and extent of Fusarium infections. From this perspective, a separate harvest in the field can be taken into consideration. Based on the application of chlorophyll fluorescence and hyperspectral imaging, characteristics, requirements and limitations of Fusarium detection on wheat, both in the field and in the laboratory, are discussed. While the modification of spectral signatures due to fungal infection allows its detection by hyperspectral imaging, the decreased physiological activity of tissues resulting from Fusarium impacts provides the base for CFI analyses. In addition, the two methods are compared in view of their usability for the detection of Fusarium, and different approaches for data analysis are presented.

show abstract

Automatic detection of ‘yellow rust’ in wheat using reflectance measurements and neural networks

Cited by 288 publications

References 10 publications

Crop Disease and Pest Monitoring by Remote Sensing

Crop Disease and Pest Monitoring by Remote Sensing

Wheat Yield Response to Foliar Fungicide Application against Leaf Rust Caused by Puccinia triticina

Hyperspectral and Chlorophyll Fluorescence Imaging for Early Detection of Plant Diseases, with Special Reference to Fusarium spec. Infections on Wheat

Contact Info

Product

Resources

About