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Author(s):

Sanika Dhanve | Manas Kale | Tejas Phadatare | Sujal Dherange
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Abstract:
Traditional Weather Forecasting Methods Often Fall Short In Delivering Hyperlocal Predictions Crucial For Agriculture, Especially In Rural Areas. Artificial Intelligence (AI) And Machine Learning (ML) Can Transform Weather Forecasting Systems By Providing Precise, Timely, And Localized Predictions. This Paper Explores How AI-based Models Improve Agricultural Outcomes For Rural Farmers By Anticipating Rainfall, Temperature, And Climatic Patterns, Enhancing Decision-making And Crop Planning. A Case Study Approach Demonstrates How AI Tools Reduce Uncertainty And Agricultural Losses While Promoting Data-driven Farming Practices. The Study Highlights The Integration Of AI With IoT, Satellite Data, And Real-time Weather Monitoring Systems To Build A Holistic Weather Advisory Ecosystem For Farmers. Furthermore, The Abstract Outlines The Practical Implementation And Effectiveness Of AI-based Forecasting Models Through A Detailed Case Study In Maharashtra. It Discusses The Role Of Government And Local Participation In Promoting AI Adoption, With Emphasis On Improving Economic Conditions For Smallholder Farmers. The Abstract Also Provides A Snapshot Of The Results, Which Suggest Tangible Improvements In Crop Planning And Yield Due To AI Intervention. These Findings Align With Earlier Studies Indicating Improved Productivity Through Neural Network Models [1].

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