Frogeye leaf spot of soybean, caused by Cercospora sojina, is typically a disease of warm and humid regions (2). Although the disease was reported in the Midwest in the 1920s (1), no outbreaks have been recorded in Iowa. Outbreaks of frogeye leaf spot occurred during 1999 in soybean fields in Ames and Grand Junction in central Iowa. During the 2000 growing season, the disease occurred in southwestern, southcentral, central, southeastern, and east-central Iowa. Occurrences of the disease with severity (reduction of green leaf area) greater than 50% were observed in production soybean fields at Grand Junction in central Iowa and Central City in eastern Iowa. In a 12-ha no-till field planted with cv. Asgrow 2501, the disease was noticeable and uniformly distributed in the entire field in mid July. Disease severity in this field was greater than 70% by the end of August. Disease incidence, however, was less than 10% in three adjacent soybean fields. In a soybean performance test at a central Iowa location where the disease occurred in 1999 and 2000, the disease was observed on all 80 varieties, with four having a severity equal to or greater than 40%. Fourteen entries had less than a 10% disease severity and 19 entries had a disease severity equal to or greater than 30%. Infected leaves in these locations had typical lesions of frogeye leaf spot, which appeared as reddish brown margins surrounding light brown or ash gray centers. On the infected tissues, hyaline, straight, and multiseptate conidia from clustered conidiophores were found, isolated, and identified to C. sojina. The relatively warm winter temperatures in 1998 to 1999 and 1999 to 2000 were associated with frogeye leaf spot epidemics. Because of the seedborne nature of C. sojina, efforts are warranted to monitor and survey the occurrence of frogeye leaf spot in Iowa, an important seed production state in the northern soybean production region. References: (1) K. Athow and A. H. Probst. Phytopathology 42:660-662, 1952. (2) D. V. Phillips. 1999. Pages 20-21 in: Soybean Disease Compendium. Hartman et al. eds, American Phytopathological Society. St. Paul, MN.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1094/PDIS.2001.85.4.443A | 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.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!