The effects of trinexapac-ethyl (TE) on the anatomical and biochemical composition of turfgrasses and their implications for its field use are poorly understood. Two greenhouse experiments were conducted to determine if application of TE increased Kentucky bluegrass (Poa pratensis L.) leaf blade cell density, chlorophyll concentration, or structural carbohydrate content. Kentucky bluegrass (KB) sod was harvested from the field, established in greenhouse pots, and sprayed with 0.27 kg·ha-1 a.i. TE. Leaf blade samples were collected 4 weeks after treatment (WAT), fixed in glutaraldehyde, and embedded in Spurr resin. Photomicrographs of longitudinal leaf blade sections were used to determine cell density, cell length, and cell width. Chlorophyll and structural carbohydrate contents were determined at 2 and 4 WAT. Treatment with TE increased cell density and chlorophyll-b concentration, while reducing cell length, but structural carbohydrate content was unaffected. Chemical name used: 4-cyclopropyl-α-hydroxy-methylene-3,5-dioxo-cyclohexanecarboxylic acid ethyl ester (trinexapac-ethyl).
We identify objects from their visually observable morphological features. Automatic methods for identifying living objects are often needed in new technology, and these methods try to utilize shapes. When it comes to identifying plant species automatically, machine vision is difficult to implement because the shapes of different plants overlap and vary greatly because of different viewing angles in field conditions. In the present study we show that chlorophyll a fluorescence, emitted by plant leaves, carries information that can be used for the identification of plant species. Transient changes in fluorescence intensity when a light is turned on were parameterized and then subjected to a variety of pattern recognition procedures. A Self-Organizing Map constructed from the fluorescence signals was found to group the signals according to the phylogenetic origins of the plants. We then used three different methods of pattern recognition, of which the Bayesian Minimum Distance classifier is a parametric technique, whereas the Multilayer Perceptron neural network and k-Nearest Neighbor techniques are nonparametric. Of these techniques, the neural network turned out to be the most powerful one for identifying individual species or groups of species from their fluorescence transients. The excellent recognition accuracy, generally over 95%, allows us to speculate that the method can be further developed into an application in precision agriculture as a means of automatically identifying plant species in the field.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.