A Journey on The Exploration of Village Plant Dataset Using ‎Machine Learning Models

  • Authors

    • K. Sujatha Department of Computer Science Engineering, Lincoln University College, Petaling Jaya, Malaysia https://orcid.org/0000-0003-2417-0760
    • Ganesh Khekare School of Computer Science & Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India https://orcid.org/0000-0002-1687-4699
    • Divya Midhunchakkaravarthy Centre of Postgraduate Studies, Lincoln University College, Petaling Jaya, Malaysia https://orcid.org/0000-0002-8044-1863
    • M. Malathi Department of ECE, Rajalakshmi Institute of Technology, Chennai, India
    • V. Srividhya Department of EEE, Meenakshi College of Engineering, Chennai, Tamil Nadu, India
    • N.P.G. Bhavani Department of ECE, Saveetha Institute of Medical and Technical Sciences, Chennai, India
    https://doi.org/10.14419/jk48hn96

    Received date: July 30, 2025

    Accepted date: September 2, 2025

    Published date: September 10, 2025

  • Plant Stress; Village Plant Dataset; Internet of Things; Ant Colony Optimization; Densenet; Resnet 50; VGG 19 and Long Short-Term Memory ‎Networks.

  • Abstract

    This article is coined for investigating the Village Plant dataset. Many researchers worldwide, carrying out their research in ‎the domain of agriculture, are dependent on this open source dataset. A plant is vulnerable to several infirmities during its period of growth. ‎Detection of the plant’s ill health and monitoring the environmental parameters is the most challenging task in agriculture. Plant disease epidemic may have a significant effect on crop production, reducing the country’s wealth. Early diagnosis of the occurrence of ill health in plants ‎and the remedies are feasible using Artificial Intelligence (AI). Currently, methods like Deep Learning (DL) algorithms, machine vision ‎techniques, and robotics play an important role in monitoring plant diseases and the growth status. This dataset contains multi-fold in-‎information about the plants. They include the normal and diseased images of plants like Bell Pepper, Tomato, Cucumber, and Potato. An Internet ‎of Things (IoT) based plant data collection and integration system will provide data for this research, which optimizes the feature set through ‎Ant Colony Optimization (ACO) for improving prediction in feature selection using deep learning models like DenseNet, ResNet 50, ‎VGG 19, and Long Short-Term Memory (LSTM) networks, which in turn enhances plant productivity with advances in AI-driven agricul‎tural diagnostics for plant stress prediction‎.

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  • How to Cite

    Sujatha , K. ., Khekare, G. ., Midhunchakkaravarthy, D. ., Malathi, M. ., Srividhya , V. ., & Bhavani, N. . (2025). A Journey on The Exploration of Village Plant Dataset Using ‎Machine Learning Models. International Journal of Basic and Applied Sciences, 14(5), 327-334. https://doi.org/10.14419/jk48hn96