Early Gender Identification of Date Palm Using Machine Learning


  • Awad Bin Naeem Department of Computer Science, National College of Business Administration & Economics, Multan, Pakistan.
  • Faiza Khalid Department of Media & Communication, National University of Modern Language Islamabad, Pakistan.
  • Abdul Majid Soomro Department of Computer Science (FSKTM), University Tun Hussein Onn Malaysia, Malaysia.
  • Armando Dacalcap Del Mundo Educational Technology Center, University of Technology and Applied Sciences Salalah, Oman.
  • Abdelhamid Zaidi Department of Mathematics, Colleg of Science, Qassim University, Buraydah, Saudia Arabia.
  • Biswaranjan Senapati Department of Computer Science and Data Science, Parker Hannifin Corp, USA.
  • Ojas Prakashbhai Doshi Department of Pharmaceutical Sciences, Arnold & Marie Schwartz College of Pharmacy and Health Sciences Brooklyn, NY, USA.


Date Palm, Machine Learning, Sex Identification, Feature Extraction


Date palm is a tree grown for its sweet edible fruit by the palm family. Palm's long-life cycle and heterozygous nature, date palm breeding is challenging. So, sex identification at seedlings is essential to overcome the cost and tidy effort of the growers. Our study proposes an efficient technique for the sex identification of Date palms at the seedling stage. We aim to use supervised Machine Learning Techniques (KNN, SVM, Naive Byes, and AdaBoost) for the sex identification of date palms. We use the feature extraction technique before classification to represent the exciting part of the image. Results indicated that the SVM algorithm is the most accurate for sex identification, with 97% accuracy. When given information about the shape of a Date palm's leaves, machine learning models can be used to figure out what the palm is. This study gives us a fast and accurate way to test for DNA markers, and it has the potential to significantly improve the selection efficiencies of date growers. Because male and female date palm genotypes can be identified before maturation, breeders' costs and time commitment are reduced. Deep learning and other methods should be evaluated for their utility in answering additional date palm sex questions. A more comprehensive database of Date palm genotype biodiversity could be created and used to support the findings presented.




How to Cite

Awad Bin Naeem, Faiza Khalid, Abdul Majid Soomro, Armando Dacalcap Del Mundo, Abdelhamid Zaidi, Biswaranjan Senapati, & Ojas Prakashbhai Doshi. (2023). Early Gender Identification of Date Palm Using Machine Learning . Journal of Computing & Biomedical Informatics, 4(02), 128–141. Retrieved from https://jcbi.org/index.php/Main/article/view/147

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