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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Amarjeet Kumar & Vijay Kumar Singh & Bhagwat Saran & Nadhir Al-Ansari & Vinay Pratap Singh & Sneha Adhikari & Anjali Joshi & Narendra Kumar Singh & Dinesh Kumar Vishwakarma, 2022. "Development of Novel Hybrid Models for Prediction of Drought- and Stress-Tolerance Indices in Teosinte Introgressed Maize Lines Using Artificial Intelligence Techniques," Sustainability, MDPI, vol. 14(4), pages 1-17, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:4:p:2287-:d:751716
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    References listed on IDEAS

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    1. Passioura, J. B., 1983. "Roots and drought resistance," Agricultural Water Management, Elsevier, vol. 7(1-3), pages 265-280, September.
    2. Xiaohu Wen & Jianhua Si & Zhibin He & Jun Wu & Hongbo Shao & Haijiao Yu, 2015. "Support-Vector-Machine-Based Models for Modeling Daily Reference Evapotranspiration With Limited Climatic Data in Extreme Arid Regions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(9), pages 3195-3209, July.
    3. Vishwakarma, Dinesh Kumar & Pandey, Kusum & Kaur, Arshdeep & Kushwaha, N.L. & Kumar, Rohitashw & Ali, Rawshan & Elbeltagi, Ahmed & Kuriqi, Alban, 2022. "Methods to estimate evapotranspiration in humid and subtropical climate conditions," Agricultural Water Management, Elsevier, vol. 261(C).
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    1. Radko Loučka & Filip Jančík & Petr Homolka & Yvona Tyrolová & Petra Kubelková & Alena Výborná & Veronika Koukolová & Václav Jambor & Jan Nedělník & Jaroslav Lang & Marie Gaislerová, 2022. "Pilot Study on Predictive Traits of Fresh Maize Hybrids for Estimating Milk and Biogas Production," Agriculture, MDPI, vol. 12(4), pages 1-10, April.
    2. Abhinav Kumar Singh & Pankaj Kumar & Rawshan Ali & Nadhir Al-Ansari & Dinesh Kumar Vishwakarma & Kuldeep Singh Kushwaha & Kanhu Charan Panda & Atish Sagar & Ehsan Mirzania & Ahmed Elbeltagi & Alban Ku, 2022. "An Integrated Statistical-Machine Learning Approach for Runoff Prediction," Sustainability, MDPI, vol. 14(13), pages 1-30, July.

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