Soil Quality Analyzer and Crop Recommendation System Using Machine Learning Algorithms for Optimizing Agricultural Productivity and Sustainability

@article{1707760,
      author = {Kavipreetha R, Kavinashri V, Nithisha N, Kalaiarasan T},
      title = {Soil Quality Analyzer and Crop Recommendation System Using Machine Learning Algorithms for Optimizing Agricultural Productivity and Sustainability},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {10},
      pages = {142-148},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1707760.pdf},
      abstract = {Agriculture is crucial to India's economy, employing about 48% of the population, yet traditional farming methods and soil depletion pose challenges to crop yield. This project aims to assist farmers by analyzing soil and pH data using machine learning to recommend suitable crops and fertilizers, addressing the lack of precision agriculture. By leveraging categorization strategies, the system enhances crop and fertilizer recommendations, aiding novice farmers. Additionally, it introduces a computer-aided disease recognition model to combat crop losses due to unidentified plant diseases, replacing time-consuming manual inspections with accurate, automated detection. The integration of modern technology in farming not only increases productivity but also optimizes resource utilization, minimizes wastage, and supports sustainable agricultural practices. This approach empowers farmers with data-driven insights, ensuring better decision-making and improved profitability in the long run.},
      keywords = {ML-Machine Learning, NEHA- National E-Health Authority, PCA-Principal Compound Analysis.},
      month = {April},
  }