Artificial Intelligence-Based Intelligent Fertigation Recommendation Systems for Precision Agriculture

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@article{1722972,
      author = {Shraddha S. Tayade, Dr. Yogesh V. Chimate},
      title = {Artificial Intelligence-Based Intelligent Fertigation Recommendation Systems for Precision Agriculture},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {3},
      pages = {1361-1371},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1722972.pdf},
      abstract = {Artificial-intelligence-enabled fertilizer and fertigation recommendation has progressed from single-task crop or fertilizer classification toward integrated decision-support architectures. However, predictive classification, agronomic dose calculation, irrigation scheduling, explainability, real-time sensing, and farmer-facing delivery are often studied separately. This paper presents a structured narrative review of these strands and an applied machine-learning case study using the publicly available Crop and Fertilizer Dataset for Western Maharashtra. The case study contains 4,513 records spanning five districts, 16 crops, and 19 fertilizer classes. Seven classifiers were compared using an 80:20 stratified train-test split with five-fold stratified cross-validation on the training set. XGBoost achieved 97.34% test accuracy and 94.21% ± 1.15% cross-validated accuracy, while Random Forest achieved 93.58% and 90.89% ± 0.80%, respectively. TreeSHAP analysis of Random Forest identified crop identity, potassium, and nitrogen as the leading predictors of the historical fertilizer class. These results are interpreted as a computational baseline rather than proof of agronomic optimality because the target label represents recorded fertilizer choices. The review also incorporates evidence on IoT/edge-cloud sensing, multilingual agricultural advisory, and federated learning. It concludes that RF/SHAP, IoT sensing, and multilingual interfaces are established capabilities; a more defensible research direction is an auditable pipeline that separates fertilizer identity, nutrient dose, and application timing, connects explainable prediction to sequential scheduling, and independently benchmarks outputs against authoritative agronomic guidance. The proposed Intelligent Fertigation Recommendation System (IFRS) is therefore presented as a research framework requiring multi-season and field validation before claims of yield, water, nutrient-use-efficiency, or adoption benefits.},
      keywords = {fertigation; precision agriculture; machine learning; explainable AI; SHAP; Q-learning},
      month = {September},
      doi = {https://doi.org/10.64388/IREV10I3-1722972}
  }