農学における生物統計学— 農業データ解析のルーツから見ていく現代の農学と統計学 —

抄録

This review provides a comprehensive introduction to recent developments in agricultural statistics. Agricultural statistics, which began with Fisher’s design of experiments, has developed in various directions as the nature of the data it handles has changed. The ability to rapidly measure omics data, including DNA sequences, has led to methods such as genomic selection. It has become possible to comprehensively measure even the metabolites of living organisms, giving birth to a new field called metabolomics. The development of machine learning, including deep learning, has enabled the use of image data, which has been difficult to connect with agriculture and is creating new areas such as disease diagnosis of crops. In this review, we first refer to the statistics of Fisher’s era, recall the philosophy of science in statistics, and look at the prospects of modern agricultural statistics by taking a broad overview of new fields.