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

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 privacypreserving 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 realworld 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 https://www.indjst.org/ forward in translating complex computational analysis into practical, on-theground tools for agriculture professionals.