Shanjun Zhu , Mengting Luo

Institute of Life Science, Jiyang College of Zhejiang A&F University, Zhuji, 311800, China
Author    Correspondence author
Cotton Genomics and Genetics, 2025, Vol. 16, No. 2   doi: 10.5376/cgg.2025.16.0006
Received: 03 Jan., 2025    Accepted: 18 Feb., 2025    Published: 01 Mar., 2025

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Zhu S.J., and Luo M.T., 2025, Comprehensive precision agriculture technology to achieve maximum cotton yield, Cotton Genomics and Genetics, 16(2): 48-56 (doi: 10.5376/cgg.2025.16.0006)

 

Cotton is an important cash crop and textile raw material globally, playing a vital role in the economies of many producing countries, but its yield growth has stagnated under conventional farming methods. This study comprehensively reviews the application of precision agriculture technologies in cotton production, focusing on key innovations such as remote sensing, GPS-guided machinery, variable rate technology (VRT), the Internet of Things (IoT), and data analytics platforms. It explores how these tools can help improve yields, resource efficiency, and environmental sustainability. The integration of big data, machine learning, and decision support systems (DSS) further enhances field decision-making, forecasting, and risk management. A case study in Xinjiang, China illustrates the real-world benefits and challenges of implementing precision agriculture in major cotton-producing regions. While these technologies have shown clear advantages in increasing productivity and reducing input costs, barriers such as high investment, technical skills gaps, and data management issues remain. Future advances in artificial intelligence, robotics, and supportive policy frameworks will play a key role in scaling up smart farming practices, ensuring sustainable and profitable cotton cultivation in the face of global agricultural challenges.

Precision agriculture; Cotton yield; Remote sensing; IoT; Decision support systems