Review On Weather-based Crop Yield Prediction Using Big Data Analytics

Abstract

Agriculture Is The Indian Economy's Backbone. Big Data Analytics Are Becoming More Precise And Feasible In Agricultural Research. Current Water Scarcity, Uncontrollable Costs Owing To Demand-supply Imbalances, And Weather Instability Need Farmers To Be Prepared With Smart Farming Techniques. Crop Yields Must Be Addressed Due To Unknown Climate Changes, Limited Irrigation Infrastructure, Soil Fertility Decrease, And Conventional Agricultural Approaches. Weather-based Crop Yield Prediction Is A Critical Area Of Agricultural Research, Providing Valuable Insights To Enhance Food Security And Optimize Resource Management. This Paper Explores The Integration Of Big Data Analytics To Predict Crop Yields Based On Weather Patterns. With The Growing Availability Of Weather-related Data From Multiple Sources, Such As Satellite Imagery, Weather Stations, And IoT Sensors, Advanced Machine Learning Algorithms And Data Mining Techniques Can Be Employed To Analyze And Predict The Impact Of Weather Variables (temperature, Rainfall, Humidity, Etc.) On Crop Production. The Study Highlights The Use Of Big Data Tools Like Hadoop, Spark, And Various Data Modeling Techniques To Process Vast Amounts Of Environmental And Agricultural Data. The Predictive Models Developed From These Data Provide Farmers, Policymakers, And Stakeholders With Actionable Insights, Allowing Them To Make Informed Decisions On Irrigation, Fertilization, Planting Schedules, And Crop Selection. This Research Demonstrates How The Fusion Of Weather Data And Big Data Analytics Can Significantly Improve Crop Yield Forecasting, Ultimately Contributing To Better Agricultural Planning, Sustainability, And Economic Growth.

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