2025, Vol. 6, Issue 1, Part A
Data-driven analysis of soybean yield enhancement through intercropping systems: Using smart agriculture tools
Author(s): Michael Thompson and Amina Hassan
Abstract: This research explores the enhancement of soybean yield through intercropping systems, leveraging smart agriculture tools for a data-driven analysis. Intercropping, as an agricultural practice, involves growing two or more crops in proximity to each other, which can potentially optimize land use and improve crop productivity. The analysis focuses on assessing the impact of various intercropping systems on soybean growth and yield, considering both environmental factors and crop interactions. The integration of smart agriculture tools, such as remote sensing, automated data collection, and artificial intelligence, allows for precise monitoring and management of intercropping systems, ensuring optimal conditions for crop development. This research employs machine learning algorithms to predict the yield outcomes based on different intercropping combinations, using historical data and real-time field data to establish correlation patterns. By focusing on the effects of intercropping systems on soybean, the research aims to identify the most effective combinations that maximize yield while maintaining soil health and sustainability. Furthermore, the role of climate data and soil health indicators in influencing soybean productivity is examined, showcasing the importance of integrating smart agricultural technologies to adapt to changing environmental conditions. The findings of this research contribute to the growing body of knowledge on sustainable agricultural practices, offering valuable insights into optimizing intercropping systems for improved crop yields, particularly soybean, which is a crucial source of protein and oil globally. This paper discusses the potential of utilizing data-driven approaches to enhance traditional farming methods and emphasizes the importance of smart agriculture tools in ensuring the future of food security.
DOI: 10.22271/27083969.2025.v6.i1a.85
Pages: 29-33 | Views: 103 | Downloads: 36
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How to cite this article:
Michael Thompson, Amina Hassan. Data-driven analysis of soybean yield enhancement through intercropping systems: Using smart agriculture tools. Int J Electr Data Commun 2025;6(1):29-33. DOI: 10.22271/27083969.2025.v6.i1a.85
