摘要
由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-调查人员发布马学习的新报告。根据新闻报道由NewsRx记者发起的印度新德里研究称，“基于天气的模拟模型”本文对赣下平原马铃薯晚疫病进行了研究使用基于人工智能的机器学习和多元线性回归算法。考虑的天气变量研究了日最高气温、最低气温、降雨量、早晨相对湿度、日最高气温、日最低气温、日降水量、日最高气温、日最低气温、日降水量、日最高相对湿度、日最高气温、日最低降雨量、日最高相对湿度、日最低相对湿度、日最高气温、日最低气温、马铃薯生长过程中观测到的夜间相对湿度、风速和太阳辐射从2006年起，随疾病参数-(CAFAD)首次出现时的作物年龄到2020年（15年）。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators publish new report on Ma chine Learning. According to news reportingoriginating in New Delhi, India, by NewsRx journalists, research stated, “Weather-based simulation modelswere devel oped in the present study to characterise late blight of potato in the Lower Gan getic Plainsusing AI-based machine learning and multiple linear regression appr oaches. Weather variables considered inthis study were, daily maximum temperatu re, minimum temperature, rainfall, morning relative humidity,evening relative h umidity, wind speed, and solar radiation, observed during the potato crop growin gperiod, along with the disease parameter - crop age at first appearance of dis ease (CAFAD) - from 2006to 2020 (15 years).”
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