Abstract
Maize (Zea mays subsp. mays) is a staple food crop in the world. Drought is one of the most common abiotic challenges that maize faces when it comes to growth, development, and production. Further knowledge of drought tolerance could aid with maize production. However, there has been less study focused on investigating in depth the drought tolerance of inbred maize lines using artificial intelligence techniques. In this study, multi-layer perceptron (MLP), support vector machine (SVM), genetic algorithm-based multi-layer perceptron (MLP-GA), and genetic algorithm-based support vector machine (SVM-GA) hybrid artificial intelligence algorithms were used for the prediction of drought tolerance and stress tolerance indices in teosinte maize lines. Correspondingly, the gamma test technique was applied to determine efficient input and output vectors. The potential of the developed models was evaluated based on statistical indices and graphical representations. The results of the gamma test based on the least value of gamma and standard error indices show that days of anthesis (DOA), days of silking (DOS), yield index (YI), and gross yield per plant (GYP) information vector arrangements were determined to be an efficient information vector combination for the drought-tolerance index (DTI) as well as the stress-tolerance index (STI). The MLP, SVM, MLP-GA, and SVM-GA algorithms’ results were compared based on statistical indices and visual interpretations that have satisfactorily predict the drought-tolerance index and stress-tolerance index in maize crops. The genetic algorithm-based hybrid models (MLP-GA and SVM-GA) were found to better predict the drought-tolerance index and stress-tolerance index in maize crops. Similarly, the SVM-GA model was found to have the highest potential to forecast the DTI and STI in maize crops, compared to the MLP, SVM, and MLP-GA models.
Highlights
Agriculture is vulnerable to climate change at the global level
In the present research work, we applied Gamma Test (GT) to find an appropriate relation between yield vector, i.e., drought-tolerance index (DTI) and stress-tolerance index (STI), and information vector, i.e., days of anthesis, days of silking, days to senescence, plant height, ear length, number of kernels, and gross yield per plant
We found an applicable relation between information and yield vector based on the least values of the gradient, SE, and gamma
Summary
Agriculture is vulnerable to climate change at the global level. Climate change is a major concern, and has adverse impacts on food production, food quality, and food security [1]. The most deleterious among abiotic stresses, which restrict optimum crop production and productivity, is considered a drought stress [2]; a total of 20–25 percent of the maize cultivation area is affected by drought throughout the world [3]. As a result, developing climate-proof genotypes as well as a drought-prediction model to battle these abiotic pressures is critical in order to feed the growing population. It was found that no effective model was developed in the past to predict drought- and stress-tolerance indices for any crops, other than the regression-based model. After reviewing the potential applications of artificial intelligence in various fields, we decide that multilayer perceptron, support vector machine, and their hybrids with genetic algorithm models can be used to develop a novel prediction model. Superior input–output combinations were developed via gamma test
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