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Background and aims: Root distribution over the soil profile is important for crop resource uptake. Using machine learning (ML), this study investigated whether measured square root of planar root length density (Sqrt_pRLD) at different soil depths were related to uptake of isotope tracer (15N) and drought stress indicator (13C) in wheat, to reveal root function. Methods: In the RadiMax semi-field root-screening facility 95 winter wheat genotypes were phenotyped for root growth in 2018 and 120 genotypes in 2019. Using the minirhizotron technique, root images were acquired across a depth range from 80 to 250 cm in May, June, and July and RL was extracted using a convolutional neural network. We developed ML models to explore whether the Sqrt_pRLD estimates at different soil depths were predictive of the uptake of deep soil nitrogen - using deep placement of 15N tracer as well as natural abundance of 13C isotope. We analyzed the correlations to tracer levels to both a parametrized root depth estimation and an ML approach. We further analyzed the genotypic effects on root function using mediation analysis. Results: Both parametrized and ML models demonstrated clear correlations between Sqrt_pRLD distribution and resource uptake. Further, both models demonstrated that deep roots at approx. 150 to 170 cm depth were most important for explaining the plant content of 15N and 13C isotopes. The correlations were higher in 2018. Conclusions: The results demonstrated that, parametrized models and ML-based analysis provided complementary insight into the importance of deep rooting for water and nitrogen uptake. [ABSTRACT FROM AUTHOR]

The stability analysis of breakwaters is very important to have a safe and economic design of these coastal protective structures and the damage level is one of the most important parameters in this context. In the recent past, machine learning techniques showed immense potential in transforming many industries and processes, for making them more efficient and accurate. In this study, five advanced machine learning algorithms, support vector regression, random forest, Adaboost, gradient boosting, and deep artificial neural network, were employed and analyzed on estimation of the damage level of rubble-mound breakwaters. A large experimental dataset, considering almost every stability variable with their whole ranges, was used in this purpose. Also, a detailed feature analysis is presented to have an insight into the relations between these variables. It was found that the present study had overcome all of the limitations of existing studies related to this field and delivered the highest level of accuracy. [ABSTRACT FROM AUTHOR]