We describe an AI recommender system (RS) with machine learning to harness past user choices and large volumes of data, yet account for changes in weather and management decisions characteristic of agricultural systems. Our goal is to maximize the use of data relevant to solving agricultural problems and improve the efficiency of the scientific workforce while also improving the accuracy of estimates of the amount of food produced. Our example shows how the RS learns data analysis choices from user behavior for predicting agricultural production responses to rainfall and learns to identify classes of agroecosystem responses to alternative climate scenarios. We account for changes in relationships using spatial and temporal statistics. The RS provides a powerful approach to make use of the large amounts of data and scientific expertise in the agricultural enterprise to predict agroecosystem dynamics under changing environmental conditions.

中文翻译：

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用于农业研究的 ML 人工智能推荐系统


我们描述了一种具有机器学习功能的人工智能推荐系统（RS），可以利用过去的用户选择和大量数据，同时考虑天气变化和农业系统的管理决策特征。我们的目标是最大限度地利用与解决农业问题相关的数据，提高科学工作者的效率，同时提高粮食产量估算的准确性。我们的示例展示了 RS 如何从用户行为中学习数据分析选择，以预测农业生产对降雨的响应，并学习识别农业生态系统对替代气候情景的响应类别。我们使用空间和时间统计来解释关系的变化。 RS 提供了一种强大的方法，可以利用农业企业中的大量数据和科学专业知识来预测不断变化的环境条件下的农业生态系统动态。