摘要
由一名新闻记者兼机器人与机器学习每日新闻的工作人员新闻编辑-机器学习的新数据在一份新的报告中呈现。根据News Rx编辑在巴西南查帕达岛的新闻报道，研究表明，“亚洲大豆锈病严重程度的传统监测是一项耗时和劳动密集型的任务，因为它需要该领域熟练的专业人员进行视觉评估。因此，在数据处理中使用遥感和机器学习(ML)技术已经成为一种提高疾病监测效率的方法，能够更快、更快地进行监测。”更准确、省时省力的评估。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Fresh data on Machine Learning are presented in a new report. According to news reporting out of Chapadao do Sul, Brazil, by News Rx editors, research stated, “Traditional monitoring of asian soybean rust severity is a time- and labor-intensive task, as it requires visual assessments by skilled professionals in the field. Thus, the use of remote sensing and machine learning (ML) techniques in data processing has emerged as an approach that can in crease efficiency in disease monitoring, enabling faster, more accurate and time - and labor-saving evaluations.”
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