Smart Crop Advisor and Disease Detector

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@article{1724755,
      author = {Yogita Navanath Shinde, Rahat Riyajkhan Pathan, Khanak Paresh Panchamiya, Tanvi Santosh Mehta, Supriya Dongare},
      title = {Smart Crop Advisor and Disease Detector},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {5},
      pages = {808-815},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1724755.pdf},
      abstract = {The agricultural economy in India is currently in a critical position, and farmers need correct information on which crops they should grow. This literature review examines how artificial intelligence and machine learning algorithms can be applied to recommend crops and detect plant diseases. The study looks at recommending crops based on soil factors and climate data and at identifying diseases from plant leaves. Previous studies show that machine learning algorithms, for example Random Forest, can be used to recommend crops, and that plant diseases can be detected from images using deep learning tools such as convolutional neural networks; however, these results can be affected by poor-quality data. The proposed application, Smart Crop Advisor and Disease Detector, has several features to support agriculture. Optical character recognition is used to extract soil data from uploaded soil reports, and a Random Forest algorithm then recommends the top three crops to plant. Plant diseases are detected using the Plant.id API. The My Farm module gives farmers crop guidance and a Daily Farm Diary, which includes an AI chatbot.},
      keywords = {Crop Recommendation; Plant Disease Detection; Soil Report OCR; Random Forest; Artificial Intelligence; Crop Selection; Farm Wise Crop; Farm Management},
      month = {November},
  }