Frogeye leaf spot (FLS), caused by the fungal pathogen K. Hara, is a foliar disease of soybean ( L. [Merr.]) responsible for yield reductions throughout the major soybean-producing regions of the world. In the United States, management of FLS relies heavily on the use of resistant cultivars and in-season fungicide applications, specifically within the class of quinone outside inhibitors (QoIs), which has resulted in the development of fungicide resistance in many states. In 2018 and 2019, 80 isolates of were collected from six counties in Georgia and screened for QoI fungicide resistance using molecular and in vitro assays, with resistant isolates being confirmed from three counties. Additionally, 50 isolates, including a "baseline isolate" with no prior fungicide exposure, were used to determine the percent reduction of mycelial growth to two fungicides, azoxystrobin and pyraclostrobin, at six concentrations: 0.0001, 0.001, 0.01, 0.1, 1, and 10 μg ml. Mycelial growth observed for resistant isolates varied significantly from both sensitive isolates and baseline isolate for azoxystrobin concentrations of 10, 1, 0.1, and 0.01 μg ml and for pyraclostrobin concentrations of 10, 1, 0.1, 0.01, and 0.001 μg ml. Moreover, 40 isolates were used to evaluate pathogen race on six soybean differential cultivars by assessing susceptible or resistant reactions. Isolate reactions suggested 12 races of present in Georgia, 4 of which have not been previously described. Species richness indicators (rarefaction and abundance-based coverage estimators) indicated that within-county race numbers were undersampled in this study, suggesting the potential for the presence of either additional undescribed races or known but unaccounted for races in Georgia. However, no isolates were pathogenic on 'Davis', a differential cultivar carrying the resistance allele, suggesting that the gene is still an effective source of resistance in Georgia.
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http://dx.doi.org/10.1094/PDIS-02-21-0236-RE | DOI Listing |
Front Plant Sci
November 2024
Department of Artificial Intelligence and Robotics, Sejong University, Seoul, Republic of Korea.
Accurately identifying apple diseases is essential to control their spread and support the industry. Timely and precise detection is crucial for managing the spread of diseases, thereby improving the production and quality of apples. However, the development of algorithms for analyzing complex leaf images remains a significant challenge.
View Article and Find Full Text PDFJ Fungi (Basel)
November 2024
Botany and Plant Pathology Department, Purdue University, West Lafayette, IN 47907, USA.
Math Biosci Eng
January 2024
Department of Mathematics, University of Tennessee at Chattanooga, 615 McCallie Avenue, Chattanooga, TN 37403, USA.
We propose a new mathematical model based on differential equations to investigate the transmission and spread of frogeye leaf spot, a major soybean disease caused by the fungus Cercospora sojina. The model incorporates the primary and secondary transmission routes of the disease as well as the intrinsic dynamics of the pathogen in the contaminated soil. We conduct detailed equilibrium and stability analyses for this model using theories of dynamical systems.
View Article and Find Full Text PDFFront Plant Sci
January 2024
College of Information Science and Technology, Hebei Agricultural University, Baoding, China.
Introduction: In precision agriculture, accurately diagnosing apple frog-eye leaf spot disease is critical for effective disease management. Traditional methods, predominantly relying on labor-intensive and subjective visual evaluations, are often inefficient and unreliable.
Methods: To tackle these challenges in complex orchard environments, we develop a specialized deep learning architecture.
Plants (Basel)
July 2023
College of Computer & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China.
Apple leaf diseases are one of the most important factors that reduce apple quality and yield. The object detection technology based on deep learning can detect diseases in a timely manner and help automate disease control, thereby reducing economic losses. In the natural environment, tiny apple leaf disease targets (a resolution is less than 32 × 32 pixel) are easily overlooked.
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