The application of artificial neural networks in modeling and predicting the effects of melatonin on morphological responses of citrus to drought stress.

Abstract

Drought stress as one of the most devastating abiotic stresses affects agricultural and horticultural productivity in many parts of the world. The application of melatonin can be considered as a promising approach for alleviating the negative impact of drought stress. Modeling of morphological responses to drought stress can be helpful to predict the optimal condition for improving plant productivity. The objective of the current study is modeling and predicting morphological responses (leaf length, number of leaves/plants, crown diameter, plant height, and internode length) of citrus to drought stress, based on four input variables including melatonin concentrations, days after applying treatments, citrus species, and level of drought stress, using different Artificial Neural Networks (ANNs) including Generalized Regression Neural Network (GRNN), Radial basis function (RBF), and Multilayer Perceptron (MLP). The results indicated a higher accuracy of GRNN as compared to RBF and MLP. The great accordance between the experimental and predicted data of morphological responses for both training and testing processes support the excellent efficiency of developed GRNN models. Also, GRNN was connected to Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to optimize input variables for obtaining the best morphological responses. Generally, the validation experiment showed that ANN-NSGA-II can be considered as a promising and reliable computational tool for studying and predicting plant morphological and physiological responses to drought stress.

Plant phenotyping relevance

植物の形態形質を予測するANNモデルを開発・比較し、検証実験と最適化まで行っており、形質推定の計算手法が研究の中心である。

Code and data availability

The paper states all relevant data are within the paper and provides no public dataset, code, or model deposit. MATLAB code and trained GRNN/NSGA-II models are not made available. Tables and figures are article content, not standalone paper-specific assets.

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