IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i4p2287-d751716.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/4/2287/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/4/2287/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Malik, Anurag & Jamei, Mehdi & Ali, Mumtaz & Prasad, Ramendra & Karbasi, Masoud & Yaseen, Zaher Mundher, 2022. "Multi-step daily forecasting of reference evapotranspiration for different climates of India: A modern multivariate complementary technique reinforced with ridge regression feature selection," Agricultural Water Management, Elsevier, vol. 272(C).
    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.
    3. Liu, Rui-Xian & Zhou, Zhi-Guo & Guo, Wen-Qi & Chen, Bing-Lin & Oosterhuis, Derrick M., 2008. "Effects of N fertilization on root development and activity of water-stressed cotton (Gossypium hirsutum L.) plants," Agricultural Water Management, Elsevier, vol. 95(11), pages 1261-1270, November.
    4. Karam, Fadi & Kabalan, Rabih & Breidi, Jolle & Rouphael, Youssef & Oweis, Theib, 2009. "Yield and water-production functions of two durum wheat cultivars grown under different irrigation and nitrogen regimes," Agricultural Water Management, Elsevier, vol. 96(4), pages 603-615, April.
    5. M. Babaei & H. Ketabchi, 2022. "Determining Groundwater Recharge Rate with a Distributed Model and Remote Sensing Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(14), pages 5401-5423, November.
    6. Rostamza, Mina & Chaichi, Mohammad-Reza & Jahansouz, Mohammad-Reza & Alimadadi, Ahmad, 2011. "Forage quality, water use and nitrogen utilization efficiencies of pearl millet (Pennisetum americanum L.) grown under different soil moisture and nitrogen levels," Agricultural Water Management, Elsevier, vol. 98(10), pages 1607-1614, August.
    7. Singh, P. & Aggarwal, P. K. & Bhatia, V. S. & Murty, M. V. R. & Pala, M. & Oweis, T. & Benli, B. & Rao, K. P. C. & Wani, S. P., 2009. "Yield gap analysis: modelling of achievable yields at farm level," IWMI Books, Reports H041995, International Water Management Institute.
    8. Li, Baoru & Zhang, Xiying & Morita, Shigenori & Sekiya, Nobuhito & Araki, Hideki & Gu, Huijie & Han, Jie & Lu, Yang & Liu, Xiuwei, 2022. "Are crop deep roots always beneficial for combating drought: A review of root structure and function, regulation and phenotyping," Agricultural Water Management, Elsevier, vol. 271(C).
    9. Thidar, Myint & Gong, Daozhi & Mei, Xurong & Gao, Lili & Li, Haoru & Hao, Weiping & Gu, Fengxue, 2020. "Mulching improved soil water, root distribution and yield of maize in the Loess Plateau of Northwest China," Agricultural Water Management, Elsevier, vol. 241(C).
    10. Rezzouk, Fatima Zahra & Gracia-Romero, Adrian & Segarra, Joel & Kefauver, Shawn C. & Aparicio, Nieves & Serret, Maria Dolors & Araus, José Luis, 2023. "Root traits and resource acquisition determining durum wheat performance under Mediterranean conditions: An integrative approach," Agricultural Water Management, Elsevier, vol. 288(C).
    11. Li, Haotian & Li, Lu & Liu, Na & Chen, Suying & Shao, Liwei & Sekiya, Nobuhito & Zhang, Xiying, 2022. "Root efficiency and water use regulation relating to rooting depth of winter wheat," Agricultural Water Management, Elsevier, vol. 269(C).
    12. Kang, Yan & Chen, Peiru & Cheng, Xiao & Zhang, Shuo & Song, Songbai, 2022. "Novel hybrid machine learning framework with decomposition–transformation and identification of key modes for estimating reference evapotranspiration," Agricultural Water Management, Elsevier, vol. 273(C).
    13. Tsakmakis, I.D. & Kokkos, N.P. & Gikas, G.D. & Pisinaras, V. & Hatzigiannakis, E. & Arampatzis, G. & Sylaios, G.K., 2019. "Evaluation of AquaCrop model simulations of cotton growth under deficit irrigation with an emphasis on root growth and water extraction patterns," Agricultural Water Management, Elsevier, vol. 213(C), pages 419-432.
    14. Kai Lun Chong & Sai Hin Lai & Yu Yao & Ali Najah Ahmed & Wan Zurina Wan Jaafar & Ahmed El-Shafie, 2020. "Performance Enhancement Model for Rainfall Forecasting Utilizing Integrated Wavelet-Convolutional Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(8), pages 2371-2387, June.
    15. Wu, Yang & Wang, Lichun & Bian, Shaofeng & Liu, Zhiming & Wang, Yongjun & Lv, Yanjie & Cao, Yujun & Yao, Fanyun & Li, Chunxia & Wei, Wenwen, 2019. "Evolution of roots to improve water and nitrogen use efficiency in maize elite inbred lines released during different decades in China," Agricultural Water Management, Elsevier, vol. 216(C), pages 44-59.
    16. Valle Júnior, Luiz C.G. & Ventura, Thiago M. & Gomes, Raphael S.R. & de S. Nogueira, José & de A. Lobo, Francisco & Vourlitis, George L. & Rodrigues, Thiago R., 2020. "Comparative assessment of modelled and empirical reference evapotranspiration methods for a brazilian savanna," Agricultural Water Management, Elsevier, vol. 232(C).
    17. Shih-Lun Fang & Yi-Shan Lin & Sheng-Chih Chang & Yi-Lung Chang & Bing-Yun Tsai & Bo-Jein Kuo, 2024. "Using Artificial Intelligence Algorithms to Estimate and Short-Term Forecast the Daily Reference Evapotranspiration with Limited Meteorological Variables," Agriculture, MDPI, vol. 14(4), pages 1-20, March.
    18. Brar, Harjeet Singh & Singh, Pritpal, 2022. "Pre-and post-sowing irrigation scheduling impacts on crop phenology and water productivity of cotton (Gossypium hirsutum L.) in sub-tropical north-western India," Agricultural Water Management, Elsevier, vol. 274(C).
    19. Bellido-Jiménez, Juan Antonio & Estévez, Javier & García-Marín, Amanda Penélope, 2021. "New machine learning approaches to improve reference evapotranspiration estimates using intra-daily temperature-based variables in a semi-arid region of Spain," Agricultural Water Management, Elsevier, vol. 245(C).
    20. Songsri, P. & Jogloy, S. & Holbrook, C.C. & Kesmala, T. & Vorasoot, N. & Akkasaeng, C. & Patanothai, A., 2009. "Association of root, specific leaf area and SPAD chlorophyll meter reading to water use efficiency of peanut under different available soil water," Agricultural Water Management, Elsevier, vol. 96(5), pages 790-798, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:4:p:2287-:d:751716. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.