PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Developing a Novel Hybrid Model for Prediction of Drought and Stress-Tolerant Indices in Teosinte Introgressed Maize Lines Using Artificial Intelligence Techniques
Version 1
: Received: 25 January 2022 / Approved: 27 January 2022 / Online: 27 January 2022 (11:21:14 CET)
How to cite:
Singh, V.K.; Kumar, A.; Saran, B.; Al-Ansari, N.; Singh, V.P.; Adhikari, S.; Joshi, A.; Singh, N.K.; Vishwakarma, D.K. Developing a Novel Hybrid Model for Prediction of Drought and Stress-Tolerant Indices in Teosinte Introgressed Maize Lines Using Artificial Intelligence Techniques. Preprints.org2022, 2022010415. https://doi.org/10.20944/preprints202201.0415.v1
Singh, V.K.; Kumar, A.; Saran, B.; Al-Ansari, N.; Singh, V.P.; Adhikari, S.; Joshi, A.; Singh, N.K.; Vishwakarma, D.K. Developing a Novel Hybrid Model for Prediction of Drought and Stress-Tolerant Indices in Teosinte Introgressed Maize Lines Using Artificial Intelligence Techniques. Preprints.org 2022, 2022010415. https://doi.org/10.20944/preprints202201.0415.v1
Cite as:
Singh, V.K.; Kumar, A.; Saran, B.; Al-Ansari, N.; Singh, V.P.; Adhikari, S.; Joshi, A.; Singh, N.K.; Vishwakarma, D.K. Developing a Novel Hybrid Model for Prediction of Drought and Stress-Tolerant Indices in Teosinte Introgressed Maize Lines Using Artificial Intelligence Techniques. Preprints.org2022, 2022010415. https://doi.org/10.20944/preprints202201.0415.v1
Singh, V.K.; Kumar, A.; Saran, B.; Al-Ansari, N.; Singh, V.P.; Adhikari, S.; Joshi, A.; Singh, N.K.; Vishwakarma, D.K. Developing a Novel Hybrid Model for Prediction of Drought and Stress-Tolerant Indices in Teosinte Introgressed Maize Lines Using Artificial Intelligence Techniques. Preprints.org 2022, 2022010415. https://doi.org/10.20944/preprints202201.0415.v1
Abstract
Maize (Zea mays subsp. mays) is the staple food crop in the world. 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 developed models was evaluated based on statistical indices and graphical representation. Results of gamma test based on the least value of gamma and standard error indices show that day of anthesis (DOA), day of silking (DOS), yield index (YI), and gross yield per plant (GYP) information vector arrangements were determined as efficient information vector combination for drought-tolerant index (DTI) as well as the stress-tolerant index (STI). The results of MLP, SVM, MLP-GA, and SVM-GA algorithms were compared based on statistical indices and visual interpretation that have satisfactory for prediction of the drought-tolerant index and stress-tolerant index in maize crop. It has also seemed that genetic algorithm-based hybrid models (MLP-GA and SVM-GA) were found a better prediction of the drought-tolerant index and stress-tolerant index in maize crop. Similarly, the SVM-GA model has the highest potential to forecast the DTI and STI in maize crops as compared to MLP, SVM, MLP-GA models.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.