Finding the suitable traits that mediate grain yield under stress condition represent an important challenge to plant breeders in order to improve production. Meaningful progress in computational analysis enables us to solve this challenge in a more efficient way. In this frame of study, our objective was to determine the most important traits associated with wheat yield improvement under aluminum stress using attribute weighting as well as supervised algorithms. To meet this goal, we studied, under normal and stress conditions, 167 bread wheat recombinant inbred lines. The lines were obtained from a cross of two semi-dwarf spring wheat varieties featured with considerable yield potential; Seri M82 and Babax. A total of 50 different traits including phenological, morphological, agronomic, physiological and biochemical traits were recorded. Ranking traits by attribute weighting algorithms indicated that among the 50 traits, five and eight traits were respectively the most probable candidates under normal and stress conditions, as highlighted by at least six weighting algorithms. According to the performance of decision tree algorithms employed, traits that were pinpointed traits by attribute weighting can be utilized to efficiently discriminate high-, medium-, and low- yield lines with up to 74.92% and 81.47% under normal and stress conditions, respectively. Moreover, valuable basis can be extrapolated from our decision tree models to guide plant breeders for selecting high yield lines going by the key traits highlighted herein. In addition, having an early prediction of yield helps farmers to make informed decision and take efficient steps to improve grain yield.

寻找合适的性状在胁迫条件下介导谷物产量，对植物育种者来说是一个重要的挑战，以提高产量。计算分析的有意义的进步使我们能够以更有效的方式解决这一挑战。在此研究框架中，我们的目标是使用属性加权和监督算法来确定铝胁迫下与小麦产量提高相关的最重要特征。为了实现此目标，我们在正常和胁迫条件下研究了167个面包小麦重组自交系。这些品系是从两个具有高产潜力的半矮春小麦品种的杂交中获得的。Seri M82和Babax。总共记录了50个不同的性状，包括物候，形态，农艺，生理和生化性状。通过属性加权算法对性状进行排名表明，在至少50个性状中，在正常和压力条件下，最可能的候选者有五个和八个性状，至少有六个加权算法强调了这一点。根据所采用决策树算法的性能，通过属性加权来精确定位特征的特征可用于有效地区分正常和胁迫条件下的高，中和低产量线，分别高达74.92％和81.47％ 。此外，可以从我们的决策树模型中推断出有价值的基础，以指导植物育种者根据此处突出显示的关键性状选择高产系。此外，对产量进行早期预测有助于农民做出明智的决定，并采取有效措施来提高谷物产量。