| Congresso Brasileiro de Microbiologia 2023 | Resumo: 1266-2 | ||||
Resumo:The plant pathogenic fungus Rhizoctonia solani is described as a complex of species, and anastomosis group 1 (AG-1) stands out as a grouping of those pathogens that adversely affect a wide number of crops around the world. In Fabaceae, the AG-1 complex causes economically important diseases, especially in the common bean (Phaseolus vulgaris L.). Meanwhile, public concerns have been raised regarding the potential health risks associated with perchlorate (ClO4-) contamination in plants, food, and the environment. Studies have shown that perchlorate accumulates in plants, in rice (Oryza sativa L.) the presence of perchlorate can inhibit plant growth. Observing the signs of deterioration caused by environmental pollutants and some phytopathogens in plants, a computational algorithm programmed in Python language was developed using image processing tools to determine the percentage of damage in leaves with signs of chlorosis. In the present study, the following stages were implemented, i) image collection, using bean (Phaseolus vulgaris) plants exposed to perchlorate, and cowpea (Vigna unguiculata) plants affected by the phytopathogenic fungus Rhizoctonia solani AG-1 IA. ii) Image processing, by implementing the OpenCV-Python package that allowed segmentation and binarization of the images. Finally, the result of the binarization was compared with an approximation of a healthy leaf, and the percentage of affected leaf area compared with the healthy leaf obtained. Meanwhile, the percentage associated with signs of chlorosis produced by perchlorate (ClO4-) and the phytopathogenic fungus Rhizoctonia solani AG-1 IA on Phaseolus vulgaris and Vigna unguiculata plants maintained under greenhouse conditions was quantified. Through the implementation of an algorithm programmed in Python language (OpenCV). The methodology employed in this research can serve as a valuable framework for future studies in various fields. For instance, it can be adapted for monitoring and diagnosing emerging plant diseases, helping farmers take proactive measures to protect their crops and prevent widespread outbreaks. Early detection and prompt action can save significant economic losses and preserve food security. Moreover, the findings hold the potential for environmental monitoring and risk assessment. By detecting and analyzing chemical substances in the environment, researchers can identify pollutants, potential hazards, and their impacts on ecosystems and human health. Palavras-chave: Chlorosis, Programming language Python, Phaseolus vulgaris, Rhizoctonia solani AG-1 IA, Perchlorate | |||||