Determining the most important physiological and agronomic traits contributing to maize grain yield through machine learning algorithms: a new avenue in intelligent agriculture

Prediction is an attempt to accurately forecast the outcome of a specific situation while using input information obtained from a set of variables that potentially describe the situation. They can be used to project physiological and agronomic processes; regarding this fact, agronomic traits such as yield can be affected by a large number of variables. In this study, we analyzed a large number of physiological and agronomic traits by screening, clustering, and decision tree models to select the most relevant factors for the prospect of accurately increasing maize grain yield. Decision tree models (with nearly the same performance evaluation) were the most useful tools in understanding the underlying relationships in physiological and agronomic features for selecting the most important and relevant traits (sowing date-location, kernel number per ear, maximum water content, kernel weight, and season duration) corresponding to the maize grain yield. In particular, decision tree generated by C&RT algorithm was the best model for yield prediction based on physiological and agronomical traits which can be extensively employed in future breeding programs. No significant differences in the decision tree models were found when feature selection filtering on data were used, but positive feature selection effect observed in clustering models. Finally, the results showed that the proposed model techniques are useful tools for crop physiologists to search through large datasets seeking patterns for the physiological and agronomic factors, and may assist the selection of the most important traits for the individual site and field. In particular, decision tree models are method of choice with the capability of illustrating different pathways of yield increase in breeding programs, governed by their hierarchy structure of feature ranking as well as pattern discovery via various combinations of features.Avat Shekoofa, Yahya Emam, Navid Shekoufa, Mansour Ebrahimi, Esmaeil Ebrahimi

Application of supervised feature selection methods to define the most important traits affecting maximum kernel water content in maize

This study presents the results of applying supervised feature selection algorithms in the selection of the most important traits contributing to the maximum kernel water content (MKWC) as a major yield component. Data were obtained from a field experiment conducted during 2008 growing season, at the Experimental Farm of the College of Agriculture, Shiraz University, and from the literature. Experiments on the subject of sink/source relationships in maize were collected from twelve fields (as records) of different parts of the world, differing in 23 characteristics (features). The feature selection algorithm demonstrated that 15 features including: planting date (days), countries (Iran, Argentina, India, USA, Canada), hybrid types, Phosphorous fertilizer applied (kg ha-1), final kernel weight (mg), soil type, season duration (days), days to silking, leaf dry weight (g plant-1), mean kernel weight (mg), cob dry weight (g plant-1), kernel number per ear, grain yield (g m-2), nitrogen applied (kg ha-1), and duration of the grain filling period (0C day) were the most effective traits in determining maximum kernel water content. Among the effective traits (features), planting date (days) revealed to be the critical one. Hybrids and countries were the second most important affecting factors on the maize kernel water content. For the first time, our results showed that features classification by supervised feature selection algorithms can provide a comprehensive view on distinguishing the important traits which contribute to maize kernel water content and yield. This study opened a new vista in maize physiology using feature selection and data mining methods and would be beneficial to newcomers of this fieldA. Shekoofa, Y. Emam, M. Ebrahimi, E. Ebrahimiehttp://www.cropj.com/february2011.htm