Crop Prediction And Soil Nutrient Monitoring System

Abstract

Precision Agriculture Plays A Crucial Role In Improving Crop Productivity While Promoting Efficient Use Of Resources. This Project Tackles Issues Such As Inconsistent Crop Yield And Improper Fertiliser Usage By Designing An Integrated System For Crop Prediction And Soil Nutrient Monitoring. The Proposed System Is Based On Internet Of Things (IoT) Technology, Where Wireless Sensors Are Installed In Agricultural Fields To Continuously Monitor Important Soil Nutrients, Including Nitrogen (N), Phosphorus (P), And Potassium (K). The Collected Data, Along With Environmental Parameters, Is Sent To A Cloud Platform For Analysis. Machine Learning Techniques, Such As Random Forest Or Support Vector Machine (SVM), Are Then Used To Recommend Suitable Crops And Forecast Expected Yields By Analysing Both Real-time Inputs And Past Climatic Trends. Additionally, The System Generates Location-specific Suggestions For Fertiliser Application And Irrigation Management, Replacing Conventional Uniform Practices With A More Precise And Data-driven Approach. The Main Goal Is To Support Farmers In Making Informed Decisions, Ultimately Increasing Productivity, Lowering Operational Costs, And Reducing Environmental Damage Caused By Excessive Use Of Fertiliser. Overall, This System Aims To Enhance Agricultural Performance While Ensuring Sustainable Management Of Natural Resources.

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