Authors
| Filter results13 paper(s) found. |
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1. Use Of Spectral Distance, Spectral Angle, And Plant Abundance Derived From Hyperspectral Imagery To Characterize Crop Growth VariationVegetation indices (VIs) derived from remote sensing imagery are commonly used to quantify crop growth and yield variations. As hyperspectral imagery is becoming more available, the number of possible VIs that can be calculated is overwhelmingly large. The objectives of this study were to examine spectral distance, spectral angle and plant abundance derived from all the bands in hyperspectral imagery and compare them with eight widely used two-band or three-band VIs based on selected wavelengths... C. Yang |
2. Multisensor Data Fusion Of Remotely Sensed Imagery For Crop Field MappingA wide variety of remote sensing data from airborne hyperspectral and multispectral images is available for site-specific management in agricultural application and production. Aerial imaging system may offer less expensive and high spatial resolution imagery with Near Infra-Red, Red, Green and Blue spectral wavebands. Hyperspectral sensor provides hundreds of spectral bands. Multisensor data fusion provides an effective paradigm for remote sensing applications by synthesizing... Y. Lan, H. Zhang, C. Yang, D. Martin, R. Lacey, Y. Huang, W.C. Hoffmann, P. Moulton |
3. Citrus Greening Disease Detection Using Airborne Multispectral And Hyperspectral ImagingCitrus greening disease (Huanglongbing or HLB) has become a major catastrophic disease in Florida’s $9 billion citrus industry since 2005, and continued to be spread to other parts of the U.S. There is no known cure for this disease. As of October 2009, citrus trees in 2,702 different sections (square mile) in 34 counties were infected in Florida. A set of hyperspectral imageries were used to develop disease detection algorithms using image-derived spectral library, the mixture tuned... W. Lee, A. Kumar, R. Ehsani, C. Yang, L.G. Albrigo, |
4. Spectral Angle Mapper (SAM) Based Citrus Greening Disease Detection Using Airborne Hyperspectral ImagingOver the past two decades, hyperspectral (HS) imaging has provided remarkable performance in ground objects classification and disease identification, due to its high spectral resolution. In this paper, a novel method named ‘extended spectral angle mapping (ESAM)’ is proposed to detect citrus greening disease (Huanglongbing or HLB), which is a destructive disease of citrus. Firstly, Savitzky-Golay smoothing filter was applied to the raw image to remove spectral noise within the data,... W. Lee, K. Wang, H. Li, R. Ehsani, C. Yang |
5. Evaluating Spectral Measures Derived From Airborne Multispectral Imagery for Detecting Cotton Root RotCotton root rot, caused by the soilborne fungus Phymatotrichopsis omnivore, is one of the most destructive plant diseases occurring... C. Yang, G.N. Odvody, C.J. Fernandez, J.A. Landivar, R.L. Nichols |
6. Using Airborne Imagery To Monitor Cotton Root Rot Infection Before And After Fungicide TreatmentCotton root rot, caused by the soilborne fungus Phymatotrichopsis omnivore, is a severe soilborne disease that has affected cotton production for over a century. Recent research has shown that a commercial fungicide, flutriafol, has potential for the control of this disease. To effectively and economically control this disease, it is necessary to identify infected areas within the field so that variable rate technology can be used to apply fungicide only to the... C. Yang, G.N. Odvody, R.R. Minzenmayer, R.L. Nichols, T. Isakeit, A. Thomasson |
7. A Comparison Of Performance Between UAV And Satellite Imagery For N Status Assessment In CornA number of platforms are available for the sensing of crop conditions. They vary from proximal (tractor-mounted) to satellites orbiting the Earth. A lot of interest has recently emerged from the access to unmanned aerial vehicles (UAVs) or drones that are able to carry sensors payloads providing data at very high spatial resolution. This study aims at comparing the performance of a UAV and satellite imagery acquired over a corn nitrogen response trial set-up. The nitrogen (N) response... P. Vigneault, N. Tremblay, M.Y. Bouroubi, C. Bélec, E. Fallon |
8. Spatial and Temporal Variation of Soil Nitrogen Within Winter Wheat Growth SeasonThis study aims to explore the spatial and temporal variation characteristics of soil ammonium nitrogen and nitrate nitrogen within winter wheat growth season. A nitrogen-rich strip fertilizer experiment with eight different treatments was conducted in 2014. Soil nitrogen samples of 20-30cm depth near wheat root were collected by in-situ Macro Rhizon soil solution collector then soil ammonium nitrogen and nitrate nitrogen content determined by SEAL AutoAnalyzer3 instrument. Classical statistics... X. Song, G. Yang, Y. Ma, R. Wang, C. Yang |
9. Field Evaluation of a Variable-rate Aerial Application SystemVariable rate aerial application systems are becoming more readily available; however, aerial applicators typically only use the systems for constant rate application of materials, allowing the systems to compensate for upwind and downwind ground speed variations. Much of the resistance to variable rate application system adoption pertains to applicator’s trust in the systems to turn on and off automatically as desired. If an application system operating in an automatic mode were... D.E. Martin, C. Yang |
10. Creating Prescription Maps from Historical Imagery for Site-specific Management of Cotton Root RotCotton root rot, caused by the soilborne fungus Phymatotrichopsis omnivore, is a severe plant disease that has affected cotton production for over a century. Recent research found that a commercial fungicide, Topguard (flutriafol), was able to control this disease. As a result, Topguard Terra Fungicide, a new and more concentrated formulation developed specifically for this market was registered in 2015, so cotton producers can use this product to control the disease. Cotton root rot only infects... C. Yang, G.N. Odvody, J.A. Thomasson, T. Isakeit, R.L. Nichols |
11. Mapping Cotton Plant Height Using Digital Surface Models Derived from Overlapped Airborne ImageryHigh resolution aerial images captured from unmanned aircraft systems (UASs) are recently being used to measure plant height over small test plots for phenotyping, but airborne images from manned aircraft have the potential for mapping plant height more practically over large fields. The objectives of this study were to evaluate the feasibility to measure cotton plant height from digital surface models (DSMs) derived from overlapped airborne imagery and compare the image-based estimates with the... C. Yang |
12. Estimating Corn Biomass from RGB Images Acquired with an Unmanned Aerial VehicleAbove-ground biomass, along with chlorophyll content and leaf area index (LAI), is a key biophysical parameter for crop monitoring. Being able to estimate biomass variations within a field is critical to the deployment of precision farming approaches such as variable nitrogen applications. With unprecedented flexibility, Unmanned Aerial Vehicles (UAVs) allow image acquisition at very high spatial resolution and short revisit time. Accordingly, there has been an increasing interest in... K. Khun, P. Vigneault, E. Fallon, N. Tremblay, C. Codjia, F. Cavayas |
13. Evaluation of Image Acquisition Parameters and Data Extraction Methods on Plant Height Estimation with UAS ImageryAerial imagery from unmanned aircraft systems (UASs) has been increasingly used for field phenotyping and precision agriculture. Plant height is one important crop growth parameter that has been estimated from 3D point clouds and digital surface models (DSMs) derived from UAS-based aerial imagery. However, many factors can affect the accuracy of aerial plant height estimation. This study examined the effects of image overlap, pixel resolution, and data extraction methods on estimation... C. Yang, C. Suh, W. Guo, H. Zhao, J. Zhang, R. Eyster |