Plant Disease Classification Using Deep Learning: A Comparative Study Using Various Machine Learning Techniques

@article{1704178,
      author = {Kushagra Sharma, Varun Goel},
      title = {Plant Disease Classification Using Deep Learning: A Comparative Study Using Various Machine Learning Techniques},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {9},
      pages = {181-185},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1704178.pdf},
      abstract = {Deep learning is an artificial intelligence subfield. With the advantages of automated learning and feature extraction, academic and industry circles have become increasingly interested in it in recent years. Image and video processing, speech processing, and natural language processing have all utilised it extensively. In addition, it has become a hub for agricultural plant protection research, including plant disease detection and pest range assessment, etc. The application of deep learning in plant disease detection may circumvent the drawbacks caused by the artificial selection of disease spot features, make the extraction of plant disease features more objective, and enhance the research efficiency and rate of technological transformation. This article describes the latest scientific advancements of deep learning technology in the identification of agricultural leaf diseases. In our study we have used images of healthy and diseased crop and used three CNN architectures to classify them as healthy and diseased crops.},
      keywords = {Plant Disease, Image Classification, Agriculture, CNN, MobileNet},
      month = {March},
  }