Determining the most important features contributing to wheat grain yield using supervised feature selection model

Abstract

A supervised feature selection algorithm was applied to determine the most important features contributing to wheat grain yield. Four hundreds seventy two fields (as records) from different parts of Iran which were different in 21 characteristics (features) were selected for feature selection analysis. Selection of the wide range of features, including location, genotype, irrigation regime, fertilizers, soil textures, physiological attitudes, and morphological characters, provided the opportunity of precise simultaneous study of a large number of factors in wheat grain yield topic by hand of data mining. The grain yield of each record assumed as target variable. The feature selection algorithm selected 14 features as the most effective features on grain yield. These features included culture type, location, soil texture, 1000 kernel weight, nitrogen supply, irrigation regime, biological yield, organic content of the soil, the amount of rainfall, genotype, plant height, and spike number per unit area. Interestingly, growing season length and plant density were the second most important features for wheat grain yield. Based on the feature selection model, culture type, as dryland farming or irrigated, severely affected wheat grain yield. The soil pH had a marginal effect on wheat grain yield. The results of this investigation demonstrated that feature classification using feature selection algorithms might be a suitable option for determining the important features contributing to wheat grain yield, providing a comprehensive view about these traits. This is the first report in identifying the most important factors on wheat grain yield from many fields using feature selection model.