Optimizing Crop Production Using Advanced Data Machine Learning Strategies in Agriculture
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Optimizing Crop Production Using Advanced Data Machine Learning Strategies in Agriculture
1. Pranaav Bhatnagar
Department of Computer Science and
Engineering,Sharda School of
Engineering and Technology,
Sharda University, Greater Noida, India
Email: pranaav03@gamil.com
2. Rishabh Bhatnagar
Team head, Technical team, Data Insight Solutions,Agra
3. Harsh vardhan Singh
Team head, implementation Team, Data Insight Solutions,Agra
4. Prakash Roy
Team head, Publication Team,Data Insight Solutions,Agra
Abstract- This project explores a cutting-edge approach to smart agriculture by integrating machine learning, deep learning, and optimisation strategies within an Internet of Things (IoT) and remote sensing framework. Leveraging real-time data acquisition from IoT sensors and satellite imagery, the system enhances prediction accuracy, processing efficiency, and scalability, surpassing the limitations of conventional IoT-based agricultural methods. By employing hybrid clustering techniques such as K-Means and DBSCAN alongside advanced deep learning models, the proposed solution significantly improves crop pattern recognition and yield forecasting capabilities. Performance evaluations demonstrate marked improvements, with accuracy reaching 94% and efficiency at 92%, underscoring the system’s potential for enabling real-time agricultural decision-making and strengthening climate resilience.
Keyword Used- Smart Agriculture, IoT, Remote Sensing, Machine Learning, Data Mining, Deep Learning, Performance Optimization, Clustering Techniques
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