scholarly journals A Plant-by-Plant Method to Identify and Treat Cotton Root Rot Based on UAV Remote Sensing

2020 ◽  
Vol 12 (15) ◽  
pp. 2453
Author(s):  
Tianyi Wang ◽  
J. Alex Thomasson ◽  
Thomas Isakeit ◽  
Chenghai Yang ◽  
Robert L. Nichols
Keyword(s):  
Remote Sensing ◽  
Root Rot ◽  
Unsupervised Classification ◽  
Kappa Coefficient ◽  
Single Plant ◽  
Multispectral Data ◽  
Cotton Root ◽  
Cotton Plants ◽  
Plant Level ◽  
Phymatotrichopsis Omnivora

Cotton root rot (CRR), caused by the fungus Phymatotrichopsis omnivora, is a destructive cotton disease that mainly affects the crop in Texas. Flutriafol fungicide applied at or soon after planting has been proven effective at protecting cotton plants from being infected by CRR. Previous research has indicated that CRR will reoccur in the same regions of a field as in past years. CRR-infected plants can be detected with aerial remote sensing (RS). As unmanned aerial vehicles (UAVs) have been introduced into agricultural RS, the spatial resolution of farm images has increased significantly, making plant-by-plant (PBP) CRR classification possible. An unsupervised classification algorithm, PBP, based on the Superpixel concept, was developed to delineate CRR-infested areas at roughly the single-plant level. Five-band multispectral data were collected with a UAV to test these methods. The results indicated that the single-plant level classification achieved overall accuracy as high as 95.94%. Compared to regional classifications, PBP classification performed better in overall accuracy, kappa coefficient, errors of commission, and errors of omission. The single-plant fungicide application was also effective in preventing CRR.

Field-region and plant-level classification of cotton root rot based on UAV remote sensing

2019 ◽  
Author(s):  
Tianyi Wang ◽  
John Alex Thomasson ◽  
Chenghai Yang ◽  
Thomas Isakeit
Keyword(s):  
Remote Sensing ◽  
Root Rot ◽  
Field Region ◽  
Cotton Root ◽  
Plant Level

scholarly journals Automatic Classification of Cotton Root Rot Disease Based on UAV Remote Sensing

2020 ◽  
Vol 12 (8) ◽  
pp. 1310 ◽  
Author(s):  
Tianyi Wang ◽  
J. Alex Thomasson ◽  
Chenghai Yang ◽  
Thomas Isakeit ◽  
Robert L. Nichols
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Root Rot ◽  
Effective Means ◽  
Unsupervised Classification ◽  
Automated Classification ◽  
Classification Methods ◽  
Automated Method ◽  
Cotton Root ◽  
Infested Field

Cotton root rot (CRR) is a persistent soilborne fungal disease that is devastating to cotton in the southwestern U.S. and Mexico. Research has shown that CRR can be prevented or at least mitigated by applying a fungicide at planting, but the fungicide should be applied precisely to minimize the quantity of product used and the treatment cost. The CRR-infested areas within a field are consistent from year to year, so it is possible to apply the fungicide only at locations where CRR is manifest, thus minimizing the amount of fungicide applied across the field. Previous studies have shown that remote sensing (RS) from manned aircraft is an effective means of delineating CRR-infested field areas. Applying various classification methods to moderate-resolution (1.0 m/pixel) RS images has recently become the conventional way to delineate CRR-infested areas. In this research, an unmanned aerial vehicle (UAV) was used to collect high-resolution remote sensing (RS) images in three Texas fields known to be infested with CRR. Supervised, unsupervised, and combined unsupervised classification methods were evaluated for differentiating CRR from healthy zones of cotton plants. Two new automated classification methods that take advantage of the high resolution inherent in UAV RS images were also evaluated. The results indicated that the new automated methods were up to 8.89% better than conventional classification methods in overall accuracy. One of these new methods, an automated method combining k-means segmentation and morphological opening and closing, provided the best results, with overall accuracy of 88.5% and the lowest errors of omission (11.44%) and commission (16.13%) of all methods considered.

Unmanned aerial vehicle remote sensing to delineate cotton root rot

2020 ◽  
Vol 14 (03) ◽  
Author(s):  
Tianyi Wang ◽  
J. Alex Thomasson ◽  
Chenghai Yang ◽  
Thomas Isakeit ◽  
Robert L. Nichols ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Root Rot ◽  
Cotton Root ◽  
Aerial Vehicle

scholarly journals Coastal Zone Classification Based on Multisource Remote Sensing Imagery Fusion

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Jiahui Li ◽  
Youxin Zhao ◽  
Jiguang Dai ◽  
Hong Zhu
Keyword(s):  
Remote Sensing ◽  
Coastal Zone ◽  
Kappa Coefficient ◽  
Observation System ◽  
High Temporal Resolution ◽  
Fusion Method ◽  
Remote Sensing Imagery ◽  
Multispectral Data ◽  
Earth Observation System ◽  
L Band

The main objective of this paper was to assess the capability of multisource remote sensing imagery fusion for coastal zone classification. Five scenes of Gaofen- (GF-) 1 optic imagery and four scenes of synthetic aperture radar (SAR) (C-band Sentinel-1 and L-band ALOS-2) imagery were collected and matched. Note that GF-1 is the first satellite of the China high-resolution earth observation system, which acquires multispectral data with decametric spatial resolution, high temporal resolution, and wide coverage. The results showed that based on the comparison of C- and L-band SAR for coastal coverage, it is verified that C band is superior to L band and parameter subsets of σvv0, σvh0, and Dcross can be effectively used for coastal classification. A new fusion method based on the wavelet transform (WT) was also proposed and used for imagery fusion. Statistical values for the mean, entropy, gradient, and correlation coefficient of the proposed method were 67.526, 7.321, 6.440, and 0.955, respectively. We therefore conclude that the result of our proposed method is superior to GF-1 imagery and traditional HIS fusion results. Finally, the classification output was determined along with an assessment of classification accuracy and kappa coefficient. The kappa coefficient and overall accuracy of the classification were 0.8236 and 85.9774%, respectively, so the proposed fusion method had a satisfying performance for coastal coverage mapping.

Changes in Trichoderma asperellum enzyme expression during parasitism of the cotton root rot pathogen Phymatotrichopsis omnivora

2015 ◽  
Vol 119 (4) ◽  
pp. 264-273 ◽  
Author(s):  
Cesar Guigón-López ◽  
Francisco Vargas-Albores ◽  
Víctor Guerrero-Prieto ◽  
Michelina Ruocco ◽  
Matteo Lorito
Keyword(s):  
Root Rot ◽  
Trichoderma Asperellum ◽  
Enzyme Expression ◽  
Cotton Root ◽  
Phymatotrichopsis Omnivora

Combining fuzzy set theory and nonlinear stretching enhancement for unsupervised classification of cotton root rot

2015 ◽  
Vol 9 (1) ◽  
pp. 096013 ◽  
Author(s):  
Huaibo Song ◽  
Chenghai Yang ◽  
Jian Zhang ◽  
Dongjian He ◽  
John Alex Thomasson
Keyword(s):  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Root Rot ◽  
Unsupervised Classification ◽  
Cotton Root ◽  
Nonlinear Stretching

scholarly journals Geomorphological Mapping of Razzaza–Habbaria Area using Remote Sensing Techniques

2018 ◽  
Vol 13 (1) ◽  
pp. 155-166
Author(s):  
Baghdad Science Journal
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Image Data ◽  
Unsupervised Classification ◽  
Multispectral Data ◽  
Geomorphological Mapping ◽  
The Earth ◽  
Sensing Application ◽  
Remote Sensing Techniques ◽  
Spatial Entities

Landforms on the earth surface are so expensive to map or monitor. Remote Sensing observations from space platforms provide a synoptic view of terrain on images. Satellite multispectral data have an advantage in that the image data in various bands can be subjected to digital enhancement techniques for highlighting contrasts in objects for improving image interpretability. Geomorphological mapping involves the partitioning of the terrain into conceptual spatial entities based upon criteria. This paper illustrates how geomorphometry and mapping approaches can be used to produce geomorphological information related to the land surface, landforms and geomorphic systems. Remote Sensing application at Razzaza–Habbaria area southwest of Razzaza Lake shows the different geomorphologic units and the land use maps that were delineated from Landsat ETM+ Image. Digital Image unsupervised classification was adopted to delineate the different classes by applying ERDAS 8.4 software. According to this classification five classes were selected and delineated in different colors.

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