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@article{163791,
        author = {Riya Pandey and Nansi Jain and Adarsh Singh and Satyam Sharma and Shuchi Shukla},
        title = {AgroInsight: An AI Powered Farmer's Associate},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {11},
        pages = {2275-2282},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=163791},
        abstract = {In the face of global challenges such as population growth, climate change, and dwindling natural resources, the agricultural sector is under immense pressure to boost productivity while minimizing environmental impact. The integration of Artificial Intelligence (AI) technologies presents a promising solution to address these challenges. The Agrao Farm Project represents a pioneering initiative that harnesses the power of AI to revolutionize traditional farming practices, optimize resource allocation, and enhance agricultural sustainability.
This abstract provides an overview of the Agrao Farm Project, focusing on its key components, objectives, and anticipated outcomes. The project leverages advanced AI algorithms and data analytics techniques to analyze diverse datasets, including weather patterns, soil composition, crop health indicators, and market demand trends. By processing and interpreting these data streams in real-time, the AI system generates actionable insights and recommendations for farmers, enabling them to make informed decisions at every stage of the farming process.
},
        keywords = {1.	Aim/Objective: a.An agro farming project using AI are to leverage cutting-edge technologies to enhance productivity, profitability, and sustainability in the agricultural sector. B.Integrate AI technologies for market analysis and forecasting to identify market trends and consumer preferences.
2.	Simulation Results: a."AI-driven agro farming project yields a 20% increase in crop yield and a 30% reduction in water usage, surpassing traditional methods." B.Predictive analytics improved decision making, resulting in a 30% decrease in fertilizer usage while maintaining soil fertility.c.AI-driven agro farming project demonstrated significant improvements in productivity
3.	Comparisons Based Analysis: a."AI-driven agro farming project surpassed traditional methods with higher yields, reduced resource usage, improved pest management, and enhanced sustainability." b.Agro farming project outperformed traditional methods by significantly enhancing yield, optimizing resource utilization, imp},
        month = {},
        }