Analyzing ML/DL Techniques for Detection of Yellow Leaf Curling and Mosaic Virus: A Systematic Literature Review
Keywords:
yellow leaf curl virus, mosaic virus, machine learning, deep learning, Hyperspectral Imaging, Generative AIAbstract
Plants play a critical role in securing global food supplies and maintaining economic stability, serving as the primary source of sustenance for both human and animal populations. Given that a substantial portion of the world's inhabitants relies on agriculture and related sectors, the impact of plant diseases cannot be overstated. Leaf diseases, in particular, pose obstacles that impede the ideal growth of plants. The timely identification of these diseases becomes paramount in order to employ interventions and minimize losses in crop yield and overall plant well-being. Such actions are essential in averting potential ecological disruptions. In the recent years, due to technological advancements machine and deep learning techniques have been increasingly used for the detection and classification of plant diseases. This study focuses on systematic literature review of various machine and deep learning techniques for the detection of Yellow Leaf Curling and Mosaic Virus of the Geminiviridae Family. The article for the review were extracted from IEEE Xplore, SCOPUS and Science Direct between 2019 to 2023. The study focused on parameters such as (1) Objective of the article (2) Data Sources and Preprocessing (3) Feature Selection or Representation (4). Model Architectures (5) Strengths and (6) Limitations. This paper suggests possible solutions of dataset creation and disease detection through hyperspectral imaging (HSI) and generative AI based on their combined capabilities.
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