SMARTCROP : AGRICULTURE CROP RECOMMENDATION BASED ON FUTURE DEMAND
Alt Text: SMARTCROP: Agriculture Crop Recommendation Based on Future Demand
Title: SMARTCROP: Agriculture Crop Recommendation Based on Future Demand
Caption: A machine learning-based crop recommendation system designed to optimize agricultural productivity and market adaptability.
Description: This study presents a predictive analytics-driven smart crop management system leveraging machine learning algorithms to analyze past data on crop yields, market trends, weather, and consumer preferences, ensuring well-informed decision-making for farmers.
Keywords: Climate conditions, Market trends, Data analysis, Machine learning, Predictive analytics, Smart agriculture
International Journal of Engineering and Techniques – Volume 10 Issue 3, May 2024
Mrs. Amitha S1, Harshal V Pai2, Joshna M J3, Kavya S4, M Jeswanth5
1Associate Professor, Department of Computer Science and Engineering, K S School of Engineering and Management, Bangalore, Karnataka, India
2Student, Department of Computer Science and Engineering, K S School of Engineering and Management, Bangalore, Karnataka, India
3Student, Department of Computer Science and Engineering, K S School of Engineering and Management, Bangalore, Karnataka, India
4Student, Department of Computer Science and Engineering, K S School of Engineering and Management, Bangalore, Karnataka, India
5Student, Department of Computer Science and Engineering, K S School of Engineering and Management, Bangalore, Karnataka, India
Abstract
Using machine learning and predictive analytics to estimate future demand, this study introduces a novel crop management approach. Predictive models are generated based on past data covering crop yields, market trends, weather conditions, and consumer preferences. The system enhances agricultural decision-making by integrating climate analysis, soil health assessment, and dynamic market trends, promoting sustainability and optimized yields. Real-time data updates and feedback loops improve adaptability to shifting market dynamics, ensuring higher productivity and financial returns for farmers.
Keywords
Climate conditions, Market trends, Data analysis, Machine learning, Predictive analytics, Smart agriculture
How to Cite
Mrs. Amitha S, Harshal V Pai, Joshna M J, Kavya S, M Jeswanth, “SMARTCROP: Agriculture Crop Recommendation Based on Future Demand,” International Journal of Engineering and Techniques, Volume 10, Issue 3, 2024. ISSN 2395-1303.
Post Comment