<p>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 [<italic>Bassia scoparia</italic> (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) [<italic>Conyza canadensis</italic> (L.) Cronquist] and common lambsquarters (<italic>Chenopodium album</italic> 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.</p>
A hyperspectral imager was used to differentiate herbicide-resistant versus herbicide-susceptible 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, herbicide-susceptible 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.
Infrared thermal imaging is a valuable tool not only in science but also in optics and photonics education and outreach activities. Observing natural optical phenomena in a different spectral region like the thermal infrared often offers new insights. The commonly used false color images not only allow extraction of useful information about thermal properties of objects, but they can also provide aesthetic sights and are thus an excellent tool for public outreach activities. Recently we have pursued this kind of study using IR imaging within Yellowstone National Park, complementing earlier work on thermal pool colors and spectroscopy. We will discuss and compare images of a variety of VIS and IR cameras of hot springs, geysers, mud pools and other natural phenomena recorded in the park during 2012 and 2016.