• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology

Article

Streamlit Application for Advanced Ensemble Learning Methods in Crop Recommendation Systems – A Review and Implementation
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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v16i48.2850

Year: 2023, Volume: 16, Issue: 48, Pages: 4688-4702

Original Article

Streamlit Application for Advanced Ensemble Learning Methods in Crop Recommendation Systems – A Review and Implementation

Yaganteeswarudu Akkem1*, Biswas Saroj Kumar2, Aruna Varanasi3

1Ph.D. Scholar, National Institute of Technology, Silchar, Assam, India
2Associate Professor, National Institute of Technology, Silchar, Assam, India
3Professor & HOD, Sreenidhi Institute of Science & Technology, Hyderabad, Telangana, India

*Corresponding Author
Email: [email protected]

Received Date:11 November 2023, Accepted Date:28 November 2023, Published Date:29 December 2023

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: This article explores the integration of advanced ensemble machine learning methods within precision agriculture, aiming to enhance the reliability and practical utility of crop recommendation systems. The incorporation of the Streamlit framework in the development process underpins our objective to deliver a user-friendly tool that provides farmers and agricultural analysts with actionable insights. Methods: A thorough literature review of artificial intelligence applications in agriculture serves as the foundation of our study, with a strong emphasis placed on sophisticated ensemble learning techniques such as stacking, an ensemble of ensembles, and federated learning. The evaluation methodology entails a comparative analysis where these cutting-edge techniques are juxtaposed against standard machine learning benchmarks to ascertain their performance improvement. In addition to the conceptual analysis, we implement a crop recommendation system using the Streamlit framework, emphasizing usability and accessibility for end-users to interact with machine learning predictions based on their soil data. Findings: The empirical results demonstrate that our chosen advanced ensemble learning methods significantly improve predictive performance, recording up to a 15% accuracy increment over traditional machine learning algorithms. Their adaptability to varied agricultural datasets, coupled with robust privacy-preserving properties, stand out. When deploying these methods in a practical Streamlit-based application, we note a marked increase of 20% in user efficiency, solidifying the system's crucial role in bolstering resilient crop management tactics. Novelty: This research pioneers the study of innovative ensemble learning techniques, married with Streamlit app development for an enhanced user experience in data-driven precision agriculture. Our findings emphasize the critical need for incorporating these advanced methodologies into real-world practices, fostering a significant paradigm shift in agricultural data analytics and management. The synergy between these powerful machine learning techniques and the Streamlit-built interactive interface represents a step forward in translating complex computational analysis into practical, on-the-ground tools for agriculture professionals.

Keywords: Machine Learning, Advanced Ensemble Learning, Streamlit.

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Copyright

© 2023 Akkem et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)

  • 08 January 2024

Year: 2023, Volume: 16, Issue: 48

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