Bioscience Evidence 2024, Vol.14, No.6, 260-269 http://bioscipublisher.com/index.php/be 267 8 Concluding Remarks The development of precision agriculture techniques for soybean yield improvement has shown significant promise across various studies. Sensor-based precision nutrient and irrigation management have been demonstrated to enhance physiological performance, water productivity, and yield of soybean crops. For instance, the adoption of sprinkler irrigation at 80% crop evapotranspiration (ETC) combined with precision nutrient management significantly improved photosynthetic characteristics and yield parameters. Additionally, the integration of unmanned aerial vehicle (UAV) platforms with machine learning algorithms has proven effective in accurately estimating soybean yield by combining maturity group information with multi-sensor data. Furthermore, the application of deep learning models, such as the 3D-ResNet-BiLSTM, has shown superior performance in predicting soybean yield at the county level, highlighting the potential of advanced computational techniques in precision agriculture. The findings from these studies have several practical implications for soybean farmers. Firstly, the use of sensor-based decision tools for nutrient and irrigation management can lead to significant improvements in crop productivity and resource-use efficiency. For example, adopting sprinkler irrigation and precision nutrient management can enhance photosynthetic rates and water-use efficiency, ultimately leading to higher yields. Secondly, the integration of UAV-based remote sensing data with machine learning models can provide farmers with accurate and timely yield predictions, enabling better decision-making and resource allocation. Lastly, the implementation of precision agriculture systems that incorporate physiological principles of crop responses can improve whole-farm yield and profit, as demonstrated in case studies. In conclusion, the advancement of precision agriculture techniques holds great potential for improving soybean yield and sustainability. The integration of sensor-based management, UAV technology, and machine learning models offers a comprehensive approach to optimizing crop production. Future research should focus on further refining these technologies and exploring their applicability across different agro-ecological regions. Additionally, there is a need for long-term studies to assess the sustainability and economic viability of precision agriculture practices. By continuing to bridge the gap between field-based physiological knowledge and advanced computational techniques, we can unlock new opportunities for enhancing crop productivity and ensuring food security. Acknowledgments The authors express deep gratitude to Professor Cai Renxiang, Researcher at the Zhejiang Agronomist College/Institute of Life Sciences for his thorough review of the manuscript and constructive suggestions. The authors also extend thanks to the two anonymous peer reviewers for their valuable revision recommendations. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Eugenio F., Grohs M., Venancio L., Schuh M., Bottega E., Ruoso R., Schons C., Mallmann C., Badin T., and Fernandes P., 2020, Estimation of soybean yield from machine learning techniques and multispectral RPAS imagery, Remote Sensing Applications: Society and Environment, 20: 100397. https://doi.org/10.1016/j.rsase.2020.100397 Fathi M., Shah-Hosseini R., and Moghimi, A., 2023, 3D-ResNet-BiLSTM model: a deep learning model for county-level soybean yield prediction with time-series Sentinel-1, Sentinel-2 imagery, and daymet data, Remote Sensing, 15(23): 5551. https://doi.org/10.3390/rs15235551 Gai, Y., Rasheed, A., Zhou, Z., Gardiner, J., Ilyas, M., Akram, M., Piwu, W., Gillani, S., Batool, M., and Jian, W., 2021, Role of conventional and molecular techniques in soybean yield and quality improvement: a critical review, Notulae Botanicae Horti Agrobotanici Cluj-Napoca, 49(4): 12555-12555. https://doi.org/10.15835/nbha49412555 Hedley C., 2015, The role of precision agriculture for improved nutrient management on farms, Journal of the Science of Food and Agriculture, 95(1): 12-19. https://doi.org/10.1002/jsfa.6734
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