Estimation of Yellow Rust in Wheat Crop Using K-Means Segmentation

2012 ◽  
Vol 2 (12) ◽  
pp. 14-16 ◽  
Author(s):  
Amina Bhaika ◽  
Keyword(s):  
2013 ◽  
Vol 42 (2) ◽  
pp. 335-342 ◽  
Author(s):  
Sujay Dutta ◽  
Suresh Kumar Singh ◽  
Mukesh Khullar

Author(s):  
G. Krishna ◽  
R. N. Sahoo ◽  
S. Pargal ◽  
V. K. Gupta ◽  
P. Sinha ◽  
...  

The potential of hyperspectral reflectance data was explored to assess severity of yellow rust disease (Biotroph Pucciniastriiformis) of winter wheat in the present study. The hyperspectral remote sensing data was collected for winter wheat (Triticum aestivum L.) cropat different levels of disease infestation using field spectroradiometer over the spectral range of 350 to 2500 nm. The partial least squares (PLS) and multiple linear (MLR) regression techniques were used to identify suitable bands and develop spectral models for assessing severity of yellow rust disease in winter wheat crop. The PLS model based on the full spectral range and n = 36, yielded a coefficient of determination (R2) of 0.96, a standard error of cross validation (SECV) of 12.74 and a root mean square error of cross validation (RMSECV) of 12.41. The validation analysis of this PLS model yielded r2 as 0.93 with a SEP (Standard Error of Prediction) of 7.80 and a RMSEP (Root Mean Square Error of prediction) of 7.46. The loading weights of latent variables from PLS model were used to identify sensitive wavelengths. To assess their suitability multiple linear regression (MLR) model was applied on these wavelengths which resulted in a MLR model with three identified wavelength bands (428 nm, 672 nm and 1399 nm). MLR model yielded acceptable results in the form of r2 as 0.89 for calibration and 0.90 for validation with SEP of 3.90 and RMSEP of 3.70. The result showed that the developed model had a great potential for precise delineation and detection of yellow rust disease in winter wheat crop.


2018 ◽  
Vol 10 (3) ◽  
pp. 410-423 ◽  
Author(s):  
Siham KHANFRI ◽  
Mohammed BOULIF ◽  
Rachid LAHLALI

Wheat (Triticum sp. L.), as one of the first domesticated food crops, is the basic staple food for a large segment of population around the world. The crop though is susceptible to many fungal pathogens. Stripe rust is an important airborne disease caused by Puccinia striiformis (Pst) and is widespread wherever wheat is cultivated throughout the world, in temperate-cool and wet environments. The causal fungus of stripe rust or yellow rust is an obligate parasite that requires another living host to complete its life cycle. Pst includes five types of spores in the life cycle on two distinct hosts. Stripe rust is distinguished from other rusts by the dusty yellow lesions that grow systemically in the form of streaks between veins and on leaf sheaths. The importance and occurrence of stripe rust disease varies in cultivated wheat, depending on environmental conditions (moisture, temperature, and wind), inoculum levels and susceptible host varieties. Transcaucasia was previously thought to be the center of origin for the pathogen. However, new findings further underlined Himalayan and near-Himalayan regions as center of diversity and a more tenable center of origin for P. striiformis. Long-distance dispersal of stripe rust pathogen in the air and occasionally by human activities enables Pst to spread to new geographical areas. This disease affects quality and yield of wheat crop. Early seeding, foliar fungicide application and cultivation of resistant varieties are the main strategies for its control. The emergence of new races of Pst with high epidemic potential which can adapt to warmer temperatures has expanded virulence profiles. Subsequently, races are more aggressive than those previously characterized. These findings emphasize the need for more breeding efforts of resistant varieties and reinforcement of other management practices to prevent and overcome stripe rust epidemic around the world.


Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7903
Author(s):  
Muhammad Hassan Maqsood ◽  
Rafia Mumtaz ◽  
Ihsan Ul Haq ◽  
Uferah Shafi ◽  
Syed Mohammad Hassan Zaidi ◽  
...  

Wheat yellow rust is a common agricultural disease that affects the crop every year across the world. The disease not only negatively impacts the quality of the yield but the quantity as well, which results in adverse impact on economy and food supply. It is highly desired to develop methods for fast and accurate detection of yellow rust in wheat crop; however, high-resolution images are not always available which hinders the ability of trained models in detection tasks. The approach presented in this study harnesses the power of super-resolution generative adversarial networks (SRGAN) for upsampling the images before using them to train deep learning models for the detection of wheat yellow rust. After preprocessing the data for noise removal, SRGANs are used for upsampling the images to increase their resolution which helps convolutional neural network (CNN) in learning high-quality features during training. This study empirically shows that SRGANs can be used effectively to improve the quality of images and produce significantly better results when compared with models trained using low-resolution images. This is evident from the results obtained on upsampled images, i.e., 83% of overall test accuracy, which are substantially better than the overall test accuracy achieved for low-resolution images, i.e., 75%. The proposed approach can be used in other real-world scenarios where images are of low resolution due to the unavailability of high-resolution camera in edge devices.


2017 ◽  
Vol 29 (2) ◽  
pp. 239
Author(s):  
Yasir Ali ◽  
Muhammad A. Khan ◽  
Muhammad Atiq ◽  
Waseem Sabir ◽  
Arslan Hafeez ◽  
...  

Wheat rusts are the significant diseases of wheat crop and potential threats worldwide. Among all major wheat diseases occurring in all wheat growing areas of the world, yellow rust caused by Puccinia striiformis f. sp. tritici is a big hazard when it occurs in severe condition. The susceptible germplasm and conducive environmental conditions contribute towards wide outbreak of rust diseases. In the present study, eight wheat lines were screened out and correlated with epidemiological factors (temperature, relative humidity, rainfall and wind speed). Results showed that maximum disease severity was observed at minimum and maximum temperature ranging from 13.7-16.7 and 23.5-27.65 0C respectively. Their disease severity was increased with increase in relative humidity ranging from 52-64 %. Similarly, rain fall ranging from 5.7-21.99 mm and wind speed 6.88-11.73 km/h respectively proved conducive for yellow rust development in Sargodha. A positive correlation was observed between disease severity and all environmental factors.


2019 ◽  
Vol 326 (1) ◽  
pp. 158-161
Author(s):  
G.M. Hasanova ◽  
◽  
Kh.N. Rustamov ◽  

Sign in / Sign up

Export Citation Format

Share Document