Inspiration
Agriculture is one of the Important pillars of Indian economy.
But, due to the recent overall degradation of its produce and quality, researchers are trying to develop various solutions to resolve the problems and needs of the farmers.
What it does
Enhancing Soil Health and Crop Productivity: Machine learning improves soil health: monitors and predicts, optimizes nutrients, detects pests, advises crop rotation, and refines irrigation. Expert collaboration is essential for success Data-Driven Smart Farming: Data collection with FAOSTAT, ML, DL (regression, VGG16) for crop modeling. IoT, sensors, automated irrigation for smart farming. Remote monitoring boosts agricultural efficiency. AI-Driven Climate Adaptation for Enhanced Crop Yields : Utilize AI to analyze historical climate and yield data, integrating real-time environmental information, and apply predictive models to optimize crop management practices in response to changing climate conditions for increased yield. AI-Enabled Direct Farmer-Consumer Ecosystem : AI fosters direct farmer-consumer connections, boosting profits and control for farmers while offering fresher, local produce for consumers, creating a mutually beneficial agricultural ecosystem.
How we built it
HTML, CSS,FLASK, PYTHON, MACHINE LEARNING,DEEP LEARNING, IOT SENSOR,SQL,JAVA SCRIPT.
Challenges we ran into
1.Managing soil degradation and nutrient loss. 2.Access to affordable and reliable technology. 3.Integrating AI into traditional farming practices. 4.Ensuring fair pricing and transparency.
What's next for AGRISAGE
We have planned to convert this design model into a working model in the future. It would be beneficial to farmers for their profit.
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