Supervised Classification of RGB Aerial Imagery to Evaluate the Impact of a Root Rot Disease
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Corey A. Moffet | Chakradhar Mattupalli | Kushendra N. Shah | Carolyn A. Young | C. Moffet | C. Young | Chakradhar Mattupalli
[1] Chunhua Zhang,et al. The application of small unmanned aerial systems for precision agriculture: a review , 2012, Precision Agriculture.
[2] Thomas Rath,et al. Improving plant discrimination in image processing by use of different colour space transformations , 2002 .
[3] R. Ansley,et al. Satellite Remote Sensing of Wheat Infected by Wheat streak mosaic virus. , 2011, Plant disease.
[4] Stephen M. Welch,et al. Plant high-throughput phenotyping using photogrammetry and imaging techniques to measure leaf length and rosette area , 2016, Comput. Electron. Agric..
[5] J. M. González-Esquiva,et al. Optimal color space selection method for plant/soil segmentation in agriculture , 2016, Comput. Electron. Agric..
[6] Jonathan P. Dash,et al. Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak , 2017 .
[7] Won Suk Lee,et al. Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees , 2013 .
[8] Anne-Katrin Mahlein. Plant Disease Detection by Imaging Sensors - Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping. , 2016, Plant disease.
[9] George P. Petropoulos,et al. A Comparison of Spectral Angle Mapper and Artificial Neural Network Classifiers Combined with Landsat TM Imagery Analysis for Obtaining Burnt Area Mapping , 2010, Sensors.
[10] P. J. Zarco-Tejada,et al. Detection of downy mildew of opium poppy using high-resolution multi-spectral and thermal imagery acquired with an unmanned aerial vehicle , 2014, Precision Agriculture.
[11] Y. Balci,et al. Bacterial Leaf Scorch in the District of Columbia: Distribution, Host Range, and Presence of Xylella fastidiosa Among Urban Trees. , 2014, Plant disease.
[12] D. Terrance Booth,et al. Very Large Scale Aerial Photography for Rangeland Monitoring , 2006 .
[13] S. Sankaran,et al. Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review , 2015 .
[14] J. Havlíček,et al. Focal Length Affects Depicted Shape and Perception of Facial Images , 2016, PloS one.
[15] Yoo-Sung Kim,et al. Automatic Shadow Removal by Illuminance in HSV Color Space , 2015 .
[16] J. M. Molina-Martínez,et al. Study and comparison of color models for automatic image analysis in irrigation management applications , 2015 .
[17] José M. Chaves-González,et al. Detecting skin in face recognition systems: A colour spaces study , 2010, Digit. Signal Process..
[18] Heping Zhu,et al. Robust Crop and Weed Segmentation under Uncontrolled Outdoor Illumination , 2011, Sensors.
[19] Edward Jones,et al. Automatic crop detection under field conditions using the HSV colour space and morphological operations , 2017, Comput. Electron. Agric..
[20] Chenghai Yang,et al. Change detection of cotton root rot infection over 10-year intervals using airborne multispectral imagery , 2016, Comput. Electron. Agric..
[21] Jose A. Jiménez-Berni,et al. Phenomic Approaches and Tools for Phytopathologists. , 2017, Phytopathology.
[22] Chenghai Yang,et al. Evaluating unsupervised and supervised image classification methods for mapping cotton root rot , 2014, Precision Agriculture.
[23] Edward Jones,et al. A survey of image processing techniques for plant extraction and segmentation in the field , 2016, Comput. Electron. Agric..
[24] David G. Schmale,et al. Tracking the potato late blight pathogen in the atmosphere using unmanned aerial vehicles and Lagrangian modeling , 2011 .
[25] D Terrance Booth,et al. Precision Measurements from Very-Large Scale Aerial Digital Imagery , 2006, Environmental monitoring and assessment.
[26] Chenghai Yang,et al. Comparison of airborne multispectral and hyperspectral imagery for mapping cotton root rot. , 2010 .