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"Turfgrasses"
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Turfgrass Salinity Stress and Tolerance—A Review
Turfgrasses are ground cover plants with intensive fibrous roots to encounter different edaphic stresses. The major edaphic stressors of turfgrasses often include soil salinity, drought, flooding, acidity, soil compaction by heavy traffic, unbalanced soil nutrients, heavy metals, and soil pollutants, as well as many other unfavorable soil conditions. The stressors are the results of either naturally occurring soil limitations or anthropogenic activities. Under any of these stressful conditions, turfgrass quality will be reduced along with the loss of economic values and ability to perform its recreational and functional purposes. Amongst edaphic stresses, soil salinity is one of the major stressors as it is highly connected with drought and heat stresses of turfgrasses. Four major salinity sources are naturally occurring in soils: recycled water as the irrigation, regular fertilization, and air-borne saline particle depositions. Although there are only a few dozen grass species from the Poaceae family used as turfgrasses, these turfgrasses vary from salinity-intolerant to halophytes interspecifically and intraspecifically. Enhancement of turfgrass salinity tolerance has been a very active research and practical area as well in the past several decades. This review attempts to target new developments of turfgrasses in those soil salinity stresses mentioned above and provides insight for more promising turfgrasses in the future with improved salinity tolerances to meet future turfgrass requirements.
Journal Article
Correction: Fall armyworm migration across the Lesser Antilles and the potential for genetic exchanges between North and South American populations
[This corrects the article DOI: 10.1371/journal.pone.0171743.].
Journal Article
Autonomous Compared with Conventional Mower Use on St. Augustinegrass Lawn Quality
Autonomous (i.e., robotic) mowers have recently garnered interest with the public and within the turfgrass industry. However, limited research has been conducted on their use for mowing warm-season turfgrasses. An experiment was conducted at the University of Florida’s West Florida Research and Education Center (Jay, FL, USA) to investigate the performance of an autonomous mower using a lower than recommended height-of-cut on St. Augustinegrass ( Stenotaphrum secundatum ). Treatments included an autonomous mower with a height-of-cut of 2.5 inches set to mow daily and a conventional mulching mower with weekly mowing at recommended height-of-cut of 3.5 inches. Data collection included weekly digital images that were subjected to digital image analysis to determine overall turfgrass quality, percent green cover, and uniformity. The autonomous mower resulted in greater overall turfgrass quality from January to March and in November, and greater green cover from November to April compared with conventional mowing. Additionally, the autonomous mower produced greater turfgrass uniformity than conventional mowing. Results indicate that autonomous mowers can be successfully used to maintain St. Augustinegrass at a lower than recommended height-of-cut.
Journal Article
A Deep-Learning-Based Real-Time Detector for Grape Leaf Diseases Using Improved Convolutional Neural Networks
Black rot, Black measles, Leaf blight and Mites of grape are four common grape leaf diseases that seriously affect grape yield. However, the existing research lacks a real-time detecting method for grape leaf diseases, which cannot guarantee the healthy growth of grape plants. In this article, a real-time detector for grape leaf diseases based on improved deep convolutional neural networks is proposed. This article first expands the grape leaf disease images through digital image processing technology, constructing the grape leaf disease dataset (GLDD). Based on GLDD and the Faster R-CNN detection algorithm, a deep-learning-based Faster DR-IACNN model with higher feature extraction capability is presented for detecting grape leaf diseases by introducing the Inception-v1 module, Inception-ResNet-v2 module and SE-blocks. The experimental results show that the detection model Faster DR-IACNN achieves a precision of 81.1% mAP on GLDD, and the detection speed reaches 15.01 FPS. This research indicates that the real-time detector Faster DR-IACNN based on deep learning provides a feasible solution for the diagnosis of grape leaf diseases and provides guidance for the detection of other plant diseases.
Journal Article
Deertongue (Dichanthelium clandestinum L.) control in golf course naturalized areas
Deertongue is a perennial, warm-season grass, and is problematic in naturalized areas of golf courses due to limited control options. Research was conducted to evaluate several herbicides for deertongue control in naturalized areas consisting primarily of fine fescue. Greenhouse studies assessed 24 herbicide admixtures and indicated that fluazifop, glyphosate, imazapic, and thiencarbazone + iodosulfuron + dicamba (TID) reduced deertongue biomass by >80% at 10 wk after initial treatment (WAIT). Subsequent field trials were conducted on golf course naturalized areas. The first site was on a woodland edge and was partially shaded for 6 h each day, and the second trial site was 50 m away from the woodland edge and not subjected to more than 1 h of daily shade. At 9 WAIT, fluazifop at 420 g ai ha-1 applied once or three times at 3-wk intervals and topramezone at 37 g ai ha-1 applied thrice at 3-wk intervals injured fine fescue by ≤10% at both sites. Glyphosate applied at 1,120 g ae ha-1, imazapic at 105 g ai ha-1, and imazapic at 53 g ai ha-1 tank-mixed with glyphosate at 560 g ae ha-1 injured fine fescue by ≥50% under shaded conditions, whereas glyphosate alone did not injure fine fescue under sunny conditions. Fine fescue was completely recovered by 52 WAIT from injury following herbicide treatments, except for glyphosate-containing treatments at the shaded site and glyphosate + imazapic at both sites. At 52 WAIT, glyphosate-containing treatments and sequential applications of fluazifop controlled deertongue by ≥93% and reduced shoot density to ≤5 shoots m–2 averaged over both sites. Fluazifop at 420 g ha-1 applied thrice at 3-wk intervals selectively controls deertongue with excellent safety to fine fescue. Glyphosate also controls deertongue, but unacceptably injures fine fescue when managed under shaded conditions. Future research will assess how different light intensities influence fine fescue epicuticular wax deposits and associated response to glyphosate. Nomenclature: Dicamba; fluazifop; glyphosate; iodosulfuron; imazapic; thiencarbazone; topramezone; deer tongue, Dichanthelium clandestinum L. (Gould); fine fescue, Festuca spp.
Journal Article