SmartAgriculture – Crop Recommendation and Plant Disease Detection System

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@article{1718718,
      author = {Dr. J. Narendra Babu, Dr. Deepak S Sakkari, Suhas A P, Srihari H S; Sriesha S G, Vaibhav M Gowda; Siddharth Vijay},
      title = {SmartAgriculture – Crop Recommendation and Plant Disease Detection System},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {12},
      pages = {1506-1509},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1718718.pdf},
      abstract = {The Smart Agriculture platform is a comprehensive intelligent farming system designed to modernize agricultural practices. The system integrates crop recommendation, soil analysis, environmental monitoring, and plant disease detection into a unified cross-platform application. The frontend application is developed using Flutter, while Firebase services provide authentication and real-time cloud synchronization. Python FastAPI is used as the backend framework for machine learning inference and API communication. The platform analyzes environmental parameters such as Nitrogen (N), Phosphorus (P), Potassium (K), temperature, humidity, and soil moisture to recommend suitable crops for cultivation. MobileNetV2 transfer learning architecture is used for soil classification, achieving >95% accuracy, while EfficientNetB4 is implemented for disease detection using crop leaf images, yielding >92% accuracy across 16 pathology classes. The system also integrates R Programming-based analytical visualization to generate crop health and disease risk reports. Experimental evaluation demonstrates high prediction accuracy and stable environmental monitoring performance under simulated IoT conditions, with an end-to-end crop recommendation accuracy of 97.3%. The proposed system contributes toward precision farming by enabling intelligent, data-driven, and sustainable agricultural practices.},
      keywords = {Smart Agriculture, Crop Recommendation, Plant Disease Detection, Artificial Intelligence, IoT Simulation, Flutter, Firebase, Deep Learning.},
      month = {June},
      doi = {https://doi.org/10.64388/IREV9I12-1718718}
  }