Bioscience Evidence 2024, Vol.14, No.6, 260-269 http://bioscipublisher.com/index.php/be 260 Research Insight Open Access Development of Precision Agriculture Techniques for Soybean Yield Improvement Yuting Zhong1,2, Shuiliang Zhong1 1 Hangzhou Shuiliang Vegetable Professional Cooperative，Xiaoshan, 311202, Zhejiang, China 2 Zhejiang Agronomist College, Hangzhou, 311202, Zhejiang, China Corresponding email: 2510246308@qq.com Bioscience Evidence, 2024, Vol.14, No.6 doi: 10.5376/be.2024.14.0027 Received: 25 Sep., 2024 Accepted: 03 Nov., 2024 Published: 17 Nov., 2024 Copyright © 2024 Zhong and Zhong, 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. Preferred citation for this article: Zhong Y.T., and Zhong S.L., 2024, Development of precision agriculture techniques for soybean yield improvement, Bioscience Evidence, 14(6): 260-269 (doi: 10.5376/be.2024.14.0027) Abstract Precision agriculture (PA) has emerged as a transformative approach to optimizing crop production, particularly for high-value crops like soybean. With the growing demand for increased soybean yields to meet global food security needs, PA technologies offer promising solutions for enhancing productivity, sustainability, and environmental stewardship. This study examines the application of various precision agriculture techniques in soybean farming, focusing on the integration of GPS, GIS, remote sensing, soil sensors, variable rate technology (VRT), and automation to improve yield efficiency. A case study of a soybean farm in the Midwest highlights the successful implementation of these technologies, demonstrating significant improvements in yield and resource management. Additionally, the study explores the role of data analytics, decision support systems, and machine learning in optimizing farm management decisions. Economic and environmental impacts, including cost-benefit analysis and sustainability, are also discussed. The findings suggest that while the adoption of precision agriculture can lead to substantial economic gains and environmental benefits, challenges remain in widespread adoption. This research provides a comprehensive overview of the potential of precision agriculture to revolutionize soybean farming, while outlining future directions for further innovation and adoption in the sector. Keywords Precision agriculture; Soybean; Yield improvement; GPS/GIS; Remote sensing; Sustainability 1 Introduction Precision agriculture (PA) is an advanced farming practice that utilizes technology to optimize field-level management regarding crop farming. The integration of sensor-based decision tools, unmanned aerial vehicles (UAVs), and machine learning algorithms has revolutionized the way farmers manage their crops. These technologies enable the precise application of nutrients and water, thereby enhancing crop productivity and resource-use efficiency. For instance, sensor-based nutrient and irrigation management has been shown to significantly improve the physiological performance and yield of soybean crops by providing real-time assessments of crop health and needs (Sachin et al., 2023a). UAV platforms equipped with multi-sensor data collection capabilities have also been employed to accurately estimate crop yields, further aiding in the optimization of farming practices (Eugenio et al., 2020; Ren et al., 2023). Soybean (Glycine max L.) is a critical crop in global agriculture due to its high protein content and versatility in food and industrial applications. It plays a vital role in food security and economic stability, particularly in regions where it is a major agricultural product. The continuous improvement of soybean yield is essential to meet the growing global food demand and address security concerns. Advances in precision agriculture techniques, such as the use of UAVs and machine learning for yield prediction, have shown promise in enhancing soybean production efficiency and sustainability (Vogel et al., 2021; Yoosefzadeh-Najafabadi et al., 2021). Moreover, understanding the physiological processes and environmental interactions that influence soybean yield can lead to more targeted and effective breeding programs (Smidt et al., 2016; Fathi et al., 2023; Wang, 2024). This study attempts to explore the development and evaluation of precision agriculture techniques to improve soybean yield, discuss the integration of advanced technologies such as UAV-based multi-sensor data and machine learning algorithms, and provide an overview of how precision breeding technologies like genome editing can
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