Volume 1 (2025) 

Big data-driven crop selection and cultivation strategy optimization: Precision agriculture for enhancing crop adaptability and yield

Chengrong Sun, Zhenjie Lu, Xiaofeng Tan

Volume 1 (2025), Article ID: eip1v0110b  

Published: 2025-01-10 (Received: 2024-10-05; Revised: 2024-12-09; Accepted: 2024-12-19) 

DOI: https://doi.org/10.5281/zenodo.15516478 

Citation

Sun C, Lu Z, Tan X. Big data-driven crop selection and cultivation strategy optimization: Precision agriculture for enhancing crop adaptability and yield. Engineering Innovation and Practice, 2025, 1, eip1v0110b.

Abstract

Global agriculture faces significant challenges due to intensifying climate change and environmental pressures. To enhance crop adaptability and yield under diverse soil conditions, this paper proposes a big data-driven method for crop selection and cultivation strategy optimization. By integrating multi-source data such as soil, climate, and crop genomes, and applying machine learning and data mining technologies, this approach identifies crop varieties resilient to drought, salinity, and heat stress, among other environmental challenges, and formulates precise cultivation strategies. The paper highlights the central role of big data technologies in optimizing crop selection and cultivation strategies, analyzes the impact of different soil types on crop growth, and outlines data-driven pathways for improving crop performance. Through case studies, the effectiveness and potential of this method for enhancing crop yield and adaptability are demonstrated. This paper aims to provide a scientifically grounded, data-driven approach for precision agriculture, supporting global crop resilience and productivity in complex environments and promoting sustainable agricultural development.

Keywords

precision agriculture, crop adaptability, big data in agriculture, cultivation strategy optimization, sustainable agriculture

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