RESUMO
Excessive accumulation of cadmium (Cd) in rice grains threatens food safety and human health. Growing low Cd accumulating rice cultivars is an effective approach to produce low-Cd rice. However, field screening of low-Cd rice cultivars is laborious, time-consuming, and subjected to the influence of environment × genotype interactions. In the present study, we investigated whether machine learning-based methods incorporating genotype and soil Cd concentration can identify high and low-Cd accumulating rice cultivars. One hundred and sixty-seven locally adapted high-yielding rice cultivars were grown in three fields with different soil Cd levels and genotyped using four molecular markers related to grain Cd accumulation. We identified sixteen cultivars as stable low-Cd accumulators with grain Cd concentrations below the 0.2 mg kg-1 food safety limit in all three paddy fields. In addition, we developed eight machine learning-based models to predict low- and high-Cd accumulating rice cultivars with genotypes and soil Cd levels as input data. The optimized model classifies low- or high-Cd cultivars (i.e., the grain Cd concentration below or above 0.2 mg kg-1) with an overall accuracy of 76%. These results indicate that machine learning-based classification models constructed with molecular markers and soil Cd levels can quickly and accurately identify the high- and low-Cd accumulating rice cultivars.