Management of Powdery Mildew on Ninebark Using Sanitizers, Biorational Products, and Fungicides

Ninebark (Physocarpus opulifolius) is a popular ornamental shrub and considered a hardy and tough plant that can thrive in different environmental conditions and resist diseases. However, powdery mildew, caused by Podosphaera physocarpi, can severelyaffect ninebark, deteriorating the ornamental value and making them unmarketable. Only a few studies have been done in managing powdery mildew of ninebark. The current study focuses on evaluating and identifying effective products (sanitizers, biorational products, and fungicides) for the management of powdery mildew disease of ninebark. A total of 12 treatments, including nontreated control, were studied. The experiment was arranged in randomized complete block design with four-single ‘Mindia Coppertina®’ ninebark plant per treatment and repeated twice. Powdery mildew disease severity, growth parameters, and phytotoxicity were assessed in the study. All treatments significantly reduced the powdery mildew disease severity and disease progress [area under disease progress curve (AUDPC)] compared with the nontreated control. The treatments, such as azoxystrobin + benzovindiflupyr at 0.17 and 0.23 g·L–1 total active ingredients (a.i.) applied, chlorothalonil + propiconazole at 1.12 mL·L–1 total a.i. applied, azoxystrobin + tebuconazole at 0.11 and 0.16 g·L–1 total a.i. applied, and giant knotweed extract [Reynoutria sachalinensis (0.5 mL·L–1 total a.i. applied)] were the most effective treatments in reducing disease severity and disease progress in both trials. The treatments had no significant effects on the plant growth parameters such as height and width. In Expt. 2, azoxystrobin + benzovindiflupyr and hydrogen peroxide + peroxyacetic acid treated plants showed the low level of phytotoxic symptoms. The phytotoxicity of these two treatments in Expt. 2 could be related to higher environmental temperature during the experimental period

Automatic Identification and Monitoring of Plant Diseases Using Unmanned Aerial Vehicles: A Review

Disease diagnosis is one of the major tasks for increasing food production in agriculture. Although precision agriculture (PA) takes less time and provides a more precise application of agricultural activities, the detection of disease using an Unmanned Aerial System (UAS) is a challenging task. Several Unmanned Aerial Vehicles (UAVs) and sensors have been used for this purpose. The UAVs’ platforms and their peripherals have their own limitations in accurately diagnosing plant diseases. Several types of image processing software are available for vignetting and orthorectification. The training and validation of datasets are important characteristics of data analysis. Currently, different algorithms and architectures of machine learning models are used to classify and detect plant diseases. These models help in image segmentation and feature extractions to interpret results. Researchers also use the values of vegetative indices, such as Normalized Difference Vegetative Index (NDVI), Crop Water Stress Index (CWSI), etc., acquired from different multispectral and hyperspectral sensors to fit into the statistical models to deliver results. There are still various drifts in the automatic detection of plant diseases as imaging sensors are limited by their own spectral bandwidth, resolution, background noise of the image, etc. The future of crop health monitoring using UAVs should include a gimble consisting of multiple sensors, large datasets for training and validation, the development of site-specific irradiance systems, and so on. This review briefly highlights the advantages of automatic detection of plant diseases to the growers

Identification of Fusarium commune, the Causal Agent of Postharvest Zinnia Meltdown Disease in Tennessee

The cut flower growers of the eastern and southern United States are threatened with postharvest meltdown of zinnia (Zinnia elegans), which reduces yield and income as well as limiting opportunities for production expansion. Disease symptoms such as bending of the stem just below the flower were visually apparent on zinnia cut flowers. The objective of this study was to identify the causal agent related to zinnia meltdown. A total of 20 symptomatic zinnia cut flower stems were collected from Tennessee. Several Fusarium-like colonies with micro and macroconidia were isolated from the base and bend area of stems on potato dextrose agar (PDA) and Fusarium-selective media. Morphological characterization, polymerase chain reaction, and sequencing of three representative isolates, FBG2020_198, FBG2020_199, and FBG2020_201, were conducted to confirm pathogen identification. The sequence identity of the isolates was \u3e99% identical to Fusarium commune, and a combined phylogenetic tree grouped the isolates with the clade of F. commune from different host and geographical locations. To accomplish Koch’s postulates, a pathogenicity test was performed on ‘Benary’s Giant Golden Yellow’, ‘Benary’s Giant Lime’, and ‘Benary’s Giant Pink’ zinnia plants at vegetative (2 weeks after transplantation) or flower bud stage (1 month after transplantation) by drench, stem injection, and foliar spray of conidial suspension (1 × 105 conidia/mL). Similar symptoms of meltdown (floral axis bending just below the flower) were observed on inoculated zinnia cultivars 2 days after harvesting. Fusarium commune was re-isolated from the infected flower stems of all three cultivars but not from the noninoculated zinnia flower stems. Zinnia stem colonization by F. commune was statistically similar in all three tested cultivars regardless of plant growth stage and method of inoculation. This study confirms F. commune as being the causal agent of postharvest zinnia flower meltdown issue in Tennessee. In the future, possible sources of pathogen will be screened, and disease management recommendations will be developed