A segment of the field of precision agriculture is being developed to accurately and quickly map the location of herbicide-resistant and herbicide-susceptible weeds using advanced optics and computer algorithms. In our previous paper, we classified herbicide-susceptible and herbicide-resistant kochia [Bassia scoparia (L.) Schrad.] using ground-based hyperspectral imaging and a support vector machine learning algorithm, achieving classification accuracies of up to 80%. In our current work, we imaged kochia along with marestail (also called horseweed) [Conyza canadensis (L.) Cronquist] and common lambsquarters (Chenopodium album L.) and the crops barley, corn, dry pea, garbanzo, lentils, pinto bean, safflower, and sugar beet, all of which were grown at the Southern Agricultural Research Center in Huntley, Montana. These plants were imaged using both ground-based and drone-based hyperspectral imagers and were classified using a neural network machine learning algorithm. Depending on what plants were imaged, the age of the plants, and lighting conditions, the classification accuracies ranged from 77% to 99% for spectra acquired on our ground-based imaging platform and from 25% to 79% on our drone-based platform. These accuracies were generally highest when imaging younger plants.
Abstract. A hyperspectral imager was used to differentiate herbicide-resistant versus herbicidesusceptible biotypes of the agronomic weed kochia, in different crops in the field at the Southern Agricultural Research Center in Huntley, Montana. Controlled greenhouse experiments showed that enough information was captured by the imager to classify plants as either a crop, herbicidesusceptible or herbicide-resistant kochia. The current analysis is developing an algorithm that will work in more uncontrolled outdoor situations. In overcast conditions, the algorithm correctly identified dicamba-resistant kochia, glyphosate-resistant kochia, and glyphosate-and dicamba-susceptible kochia with 67%, 76%, and 80% success rates, respectively. © The Authors.Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
The citizen Continental-America Telescopic Eclipse (CATE) Experiment was a new type of citizen science experiment designed to capture a time sequence of white-light coronal observations during totality from 17:16 to 18:48 UT on 2017 August 21. Using identical instruments the CATE group imaged the inner corona from 1 to 2.1 RSun with 1.″43 pixels at a cadence of 2.1 s. A slow coronal mass ejection (CME) started on the SW limb of the Sun before the total eclipse began. An analysis of CATE data from 17:22 to 17:39 UT maps the spatial distribution of coronal flow velocities from about 1.2 to 2.1 RSun, and shows the CME material accelerates from about 0 to 200 km s−1 across this part of the corona. This CME is observed by LASCO C2 at 3.1–13 RSun with a constant speed of 254 km s−1. The CATE and LASCO observations are not fit by either constant acceleration nor spatially uniform velocity change, and so the CME acceleration mechanism must produce variable acceleration in this region of the corona.
We have applied a Bayesian generalized Lomb–Scargle period searching algorithm to movies of coronal loop images obtained with the Hinode X-ray Telescope (XRT) to search for evidence of periodicities that would indicate resonant heating of the loops. The algorithm makes as its only assumption that there is a single sinusoidal signal within each light curve of the data. Both the amplitudes and noise are taken as free parameters. It is argued that this procedure should be used alongside Fourier and wavelet analyses to more accurately extract periodic intensity modulations in coronal loops. The data analyzed are from XRT Observation Program #129C: “MHD Wave Heating (Thin Filters),” which occurred during 2006 November 13 and focused on active region 10293, which included coronal loops. The first data set spans approximately 10 min with an average cadence of 2 s, 2″ per pixel resolution, and used the Al-mesh analysis filter. The second data set spans approximately 4 min with a 3 s average cadence, 1″ per pixel resolution, and used the Al-poly analysis filter. The final data set spans approximately 22 min at a 6 s average cadence, and used the Al-poly analysis filter. In total, 55 periods of sinusoidal coronal loop oscillations between 5.5 and 59.6 s are discussed, supporting proposals in the literature that resonant absorption of magnetic waves is a viable mechanism for depositing energy in the corona.
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