Agriculture Management System Using Machine Learning

• Author(s):

  Mrs.V.Hemalatha | N.Rejiya Sulthana | S.Priya | A.Pavithra

• Keywords:

  Machine Learning Techniques, Agriculture Management System, Data Driven Farming, Soil Nutrient Analysis (N,P,K), Yield Forecasting, Predictive Modeling, Historical Crop Data.

• Abstract:

  The Agriculture Management System (AMS) Is A Smart, Machine Learning-based Platform That Assists Farmers In Making Data-driven Decisions For Crop Selection, Fertilizer Use, Irrigation Planning, And Yield Estimation. It Analyzes Key Factors Such As Soil Nutrients (N, P, K), Temperature, Humidity, PH, Rainfall, And Historical Yield Data To Deliver Personalized Recommendations. Featuring A Responsive Bootstrap 4 Interface, AMS Ensures Smooth Access Across Devices, Allowing Users To Input Real-time, Location-specific Data For Tailored Insights That Enhance Productivity And Resource Efficiency. The System Integrates Weather APIs For Dynamic, Context-aware Guidance, Helping Farmers Adapt Practices To Current And Forecasted Conditions. A Built-in Agriculture Chatbot Provides 24/7 Support On Topics Like Pest Control, Organic Farming, And Crop Health. An Intelligent Irrigation Calendarfurther Optimizes Water Use By Generating Schedules Based On Crop Type, Soil, And Local Weather. Additionally, The System Stores User Data Securely, Enabling Farmers To Track Their Seasonal Progress And Refine Strategies Over Time. It Supports Multilingual Interfaces To Reach Farmers Across Diverse Regions. The Modular Design Also Allows For Future Integration With Government Schemes And Market Price Updates. In Essence, AMS Empowers Modern Agriculture By Combining AI, Real-time Data, And Intuitive Design To Boost Efficiency And Support Informed Farming Decisions.

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Other Details

• Paper id:

  IJSARTV11I5103547

• Published in:

  Volume: 11 Issue: 5 May 2025

• Publication Date:

  2025-05-13

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