首页|New Machine Learning Research from Uttar Banga Krishi Viswavidyalaya Outlined (P rediction of potato late blight disease incidence based on weather variables usi ng statistical and machine learning models: A case study from West Bengal)
New Machine Learning Research from Uttar Banga Krishi Viswavidyalaya Outlined (P rediction of potato late blight disease incidence based on weather variables usi ng statistical and machine learning models: A case study from West Bengal)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on artificial intelligen ce have been presented. According to news reporting from the Uttar Banga Krishi Viswavidyalaya by NewsRx journalists, research stated, “Late blight is one of th e most devastating diseases on potato the world over, including West Bengal, Ind ia.” The news journalists obtained a quote from the research from Uttar Banga Krishi Viswavidyalaya: “The economic and yield losses from outbreaks of potato late bli ght can be huge. In this article, application of statistical models such as auto regressive integrated moving average (ARIMA), autoregressive integrated moving a verage with exogenous variables (ARIMAX) in combination with machine learning mo dels such as, neural network auto regression (NNAR), support vector regression ( SVR) and classification and regression tree (CART) have been explored to predict the percentage disease index (PDI) of potato late blight in the northern part o f West Bengal. Models were developed to predict PDI at 3- and 7-days interval us ing the weather variables viz., rainfall, maximum and minimum temperature, maxim um and minimum relative humidity, and dew point temperature. Among the developed models, CART to predict PDI at 7 days interval was found to be the best fitted model on the basis of least RMSE, MAE and MAPE.”
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