Safar Nikmaram, Mehdi Sadravi, Mostafa Ghaderi Zefrehee, Volume 13, Issue 1 ((Autumn & Winter) 2024)
Abstract
Nikmaram, S., Sadravi, M., & Ghaderi Zefrehee, M.(2024).The impact of three arbuscular mycorrhizal fungi on wheat take-all disease caused by Gaeumannomyces graminis var. tritici. Plant Pathology Science, 13(1), 104-112.
Take-all caused by soil-borne fungus Gaeumannomyces graminis var. tritici is one of the most important diseases of wheat in the world, reported to cause up to 50% yield losses. The disease has also been reported from different areas of wheat cultivation in Iran. Biological control is a healthy and environment-friendly method for managing plant diseases, and arbuscular mycorrhizal fungi can play an important role in this field. This research was conducted to determine the effect of three arbuscular mycorrhizal fungi on the severity of this disease. The pathogen was isolated from diseased wheat plants in Kigiluyeh and Boyar-Ahmad Province, southwestern Iran. The effect of three arbuscular mycorrhizal fungi; Funneliformis mosseae, Rhizoglomus intraradices, and Blaszkowskia deserticola alone, and in combination on the disease severity and growth indices of wheat was tested under greenhouse conditions in a completely randomized design. All treatments of mycorrhizal fungi reduced disease severity and increased growth indices compared to control plants, but F. mosseae was more effective than others. Therefor
F. mosseae can be used to reduce the severity of the disease and improve the growth indices of wheat.
Mohammad Ali Hooshyar, Mehdi Sadravi, Rasool Rezaei , Volume 13, Issue 2 ((Spring and Summer) 2024)
Abstract
Rhizoctonia root rot, caused by the soil-borne fungus Rhizoctonia solani, is an important disease of beans, which has been reported from different parts of Iran. The disease has been reported to cause damage to up to 60% of the crop worldwide. Biological control can be a healthy and environmentally friendly method for managing plant diseases. This study was conducted to investigate the effect of four commercial biological products available in the Iranian market on growth indices and severity of Rhizoctonia root rot in beans to find a suitable method for biological control of the disease. The effect of three biological products of arbuscular mycorrhizal fungi including Funeliformis mosseae, Rhizoglomus intraradices and Mycopersica (a mixture of several mycorrhizal fungi) and the bacterium Bradyrhizobium japonicum on growth indices and severity of Rhizoctonia root rot in Kosha pinto-bean cultivar was tested under greenhouse conditions. Statistical analysis of the data from this experiment showed that these treatments had a significant effect on reducing disease severity and plant growth indices, and among them, F. mosseae and Mycopersica caused the greatest reduction in disease severity and improved plant growth indices, respectively. Therefore, the biological product of F. mosseae and Mycopersica can be used to reduce the severity of Rhizoctonia root rot disease in beans and improve its growth indices.
Mehdi Sadravi, Volume 14, Issue 1 ((Autumn & Winter) 2025)
Abstract
Diseases are a serious threat to the sustainable and healthy production of plant yields and the food security of the world's people, and annually reduce a significant part of their production in terms of quantity and quality. Classical methods of diagnosing plant diseases based on pathogen isolation in the laboratory and visual field monitoring of disease progression and implementation of management methods are very time-consuming, require specialized personnel and expensive. Artificial intelligence-based software uses image analysis, environmental sensors and disease forecasting modeling to quickly diagnose diseases at the farm level, integrate meteorological data, soil and crop parameters to predict the time of disease outbreak, provide appropriate suggestions for disease management based on previously available data and implement management methods quickly and accurately using the Internet of Things, drones and robots. Artificial intelligence-based softwares, help to quickly and accurately diagnose plant diseases before they cause damage, establish predictive systems for diseases, predict the exact time of their occurrence and spread, and implement timely and correct management methods, enabling increased production of healthy plant products, while reducing costs to ensure food security for the world's people.
