摘要
由一名新闻记者兼机器人与机器学习每日新闻编辑每日新闻-关于人工智能ce的详细数据已经呈现。根据NewsRx记者从Uttar Banga Krishi Viswavidyalaya的新闻报道，研究表明，"晚疫病是世界上马铃薯上最具毁灭性的疾病之一，包括印第安纳州西孟加拉邦。"新闻记者从Uttar Banga Krishi Viswavidyalaya的研究中获得了一句话：“马铃薯晚疫病爆发造成的经济损失和产量损失可能是巨大的，本文应用了自回归积分移动平均(ARIMA)、自回归积分移动平均(ARIMAX)等统计模型，并结合机器学习模型，如神经网络自回归(NNAR)、神经网络自回归(NNAR)、神经网络自回归(NNAR)等。”采用支持向量回归(SVR)和分类回归树(CART)对西孟加拉邦北部马铃薯晚疫病百分病情指数(PDI)进行预测,建立了以降雨量、最高最低气温、最高最低相对湿度、露点温度为气象变量的预测模型。在RMSE、MAE和MAPE最小的基础上，发现CART预测7天间隔的PDI是最好的拟合模型。
Abstract
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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