Development of Novel Hybrid Models for Prediction of Drought- and Stress-Tolerance Indices in Teosinte Introgressed Maize Lines Using Artificial Intelligence Techniques

Amarjeet Kumar, Vijay Kumar Singh, Bhagwat Saran, Nadhir Al-Ansari, Vinay Pratap Singh, Sneha Adhikari, Anjali Joshi, Narendra Kumar Singh and Dinesh Kumar Vishwakarma
Additional contact information
Amarjeet Kumar: Department of Genetics and Plant Breeding, MTTC & VTC, Selesih, Central Agricultural University, Imphal 795004, Manipur, India
Vijay Kumar Singh: Faculty of Agriculture Science and Technology, Mahatma Gandhi Kashi Vidhyapith, Varanasi 221002, Uttar Pradesh, India
Bhagwat Saran: Department of Soil and Water Conservation Engineering, College of Technology, G.B. Pant University of Agriculture and Technology, Pantnagar 263145, Uttarakhand, India
Nadhir Al-Ansari: Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 97187 Lulea, Sweden
Vinay Pratap Singh: Department of Plant Physiology, College of Agriculture—Ganj Basoda, Vidisha 464221, Madhya Pradesh, India
Sneha Adhikari: ICAR—Regional Station, Indian Institute of Wheat and Barley Research, Regional Station Flowerdale, Shimla 171002, Himachal Pradesh, India
Anjali Joshi: Genetics and Tree Improvement Division, Arid Forest Research Institute, Jodhpur 342005, Rajasthan, India
Narendra Kumar Singh: Department of Genetics and Plant Breeding, G.B. Pant University of Agriculture and Technology, Pantnagar 263145, Uttarakhand, India
Dinesh Kumar Vishwakarma: Department of Irrigation and Drainage Engineering, College of Technology, G.B. Pant University of Agriculture and Technology, Pantnagar 263145, Uttarakhand, India

Sustainability, 2022, vol. 14, issue 4, 1-17

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.

Keywords: drought-tolerance index; stress-tolerance index; MLP; SVM; MLP-GA; SVM-GA; genetic algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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