Brazilian scientists have developed\u00a0<\/span>a platform<\/span>\u00a0To diagnose Asian soybean rust, one of the most severe diseases affecting the crop, the technology integrates artificial intelligence (AI) with the combined analysis of climatic, agronomic, and digital image data. The cloud-based system assesses the risk of disease occurrence and generates reports with technical management recommendations, contributing to more precise decisions in the field.<\/span><\/p>\n The tool collects data from environmental sensors, digital leaf images, and agronomic parameters such as cultivar, spacing, and sowing time. The results are presented on an online dashboard, allowing farmers to track time series of climate data and plant images.<\/span><\/p>\n The system was developed as part of the project.\u00a0<\/span>Advanced Digital Tool for Agricultural Risk Management<\/span>\u00a0, with funding from the S\u00e3o Paulo State Research Foundation (\u00a0<\/span>Fapesp<\/span>\u00a0The initiative was part of the doctoral research of computer scientist Ricardo Alexandre Neves at the Federal University of S\u00e3o Carlos (\u00a0<\/span>UFSCar<\/span>\u00a0), with\u00a0<\/span>Paulo Cruvinel<\/span>\u00a0, researcher at\u00a0<\/span>Embrapa Instrumentation<\/span>\u00a0(SP), as advisor.\u00a0<\/span><\/p>\n The study\u00a0<\/span>"A cloud-based intelligence system for risk analysis of Asian soybean rust in soybean crops"<\/span>\u00a0It was published in July 2025 by the magazine.\u00a0<\/span>AgriEngineering<\/span>\u00a0.\u00a0<\/span><\/p>\n Photo: Ricardo Alexandre Neves<\/p>\n<\/div>\n Scientists developed the system through research on farms, using a model that incorporates climatic variables, data related to soybean plants, and data obtained from digital images of soybean leaves. The climatic variables were observed during the area's monitoring period.<\/span><\/p>\n \u201cThe technology classifies the favorability of the disease into three levels \u2014 low, medium, and high \u2014 depending on the combination of variables related to the stage of infestation. This allows for more effective and precise diagnoses and prognoses for disease control,\u201d adds Neves. According to him, the favorability level is defined by statistical inference based on the behavior of the set of variables.\u00a0<\/span><\/p>\n Photo: Mateus Dias\/Aprosoja MT<\/p>\n<\/div>\n The researchers explain that the system works by combining data. The main data allows for the analysis of factors essential to the development of the fungus, such as the period of leaf moisture \u2014 relative humidity above 90%, in the temperature range between 15\u00b0C and 28\u00b0C \u2014 or the dew point.\u00a0<\/span><\/p>\n This work utilizes advanced and specific processing techniques to extract information from digital images of soybean leaves. Color patterns, such as green, yellow, and brown, are associated with the stages of disease progression.<\/span><\/p>\n Cruvinel reports that, to integrate the data, the study evaluated two methods. In the end, the choice for the system fell on the Hidden Markov Chain model, which provides robustness, effectiveness, and efficiency to the decision-making process. This methodology proved superior to fuzzy logic, achieving 100% accuracy in matching the evaluated scenarios for the risk of Asian soybean rust occurrence in soybean cultivation areas.\u00a0<\/span><\/p>\n "The model developed to combine data from different variables made it possible to structure a complete set of rules that systematically considers different situations in which the disease is likely to occur," says the researcher.<\/span><\/p>\n During the four-year study with the conventional cultivar\u00a0<\/span>BRS 536 soybean variety from Embrapa<\/span>\u00a0The researchers used more than 2 gigabytes of data per crop cycle, considering information collected in real fields during cultivation, in georeferenced plots in the Poxor\u00e9u-MT region and photographed under known light levels.\u00a0<\/span><\/p>\n![](\"https:\/\/www.embrapa.br\/documents\/10180\/106428292\/260303_IAFerrugemSoja_Ricardo+Alexandre+Neves_Estrutura+da+Plataforma+para+Gesta%CC%83o+de+Riscos+decorrentes+da+Ferrugem+Asia%CC%81tica.jpg\/66e75398-d35c-1423-26dc-8d829078b676?t=1772130643026\")
<\/a><\/p>\nData fusion facilitates diagnosis.<\/span><\/h4>\n
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