Bioscience Evidence 2024, Vol.14, No.6, 260-269 http://bioscipublisher.com/index.php/be 266 et al., 2016). Additionally, integrating crop physiology principles into PA systems can bridge yield gaps and increase farmer profit while reducing risk (Monzon et al., 2018). The combination of rhizobium inoculation and phosphorus supplementation has also been found to significantly increase soybean yields in the savanna areas of Nigeria, highlighting the potential for sustainable yield improvements through precision agriculture (Jemo et al., 2023). Precision agriculture techniques offer substantial economic and environmental benefits for soybean farming. By optimizing input use and improving yield prediction accuracy, these practices can enhance profitability and promote sustainable farming. The integration of advanced technologies such as UAVs, sensor-based irrigation, and nutrient management systems further underscores the potential for sustainable soybean farming practices. 7 Future Directions and Innovations 7.1 Emerging technologies in precision agriculture Emerging technologies in precision agriculture are revolutionizing the way soybean yield is managed and improved. Sensor-based decision tools, for instance, have shown significant promise in enhancing the physiological performance and water productivity of soybean crops. These tools provide quick assessments of nutritional and physiological health, leading to better crop growth indices and yield improvements (Sachin et al., 2023b). Additionally, the integration of machine learning techniques with multispectral imagery from UAVs has proven effective in accurately predicting soybean yields, offering a robust tool for high-throughput phenotyping and crop field management (Eugenio et al., 2020; Maimaitijiang et al., 2020; Ren et al., 2023). The use of deep learning models, such as self-normalizing neural networks, further enhances yield prediction accuracy, making it a critical component of future precision agriculture practices (Shu, 2020). 7.2 The role of blockchain and IoT in agriculture Blockchain and the Internet of Things (IoT) are poised to play transformative roles in agriculture. IoT systems, in particular, are expected to significantly increase farm yields by enabling precise monitoring and management of agricultural processes. These systems can track various parameters such as soil moisture, nutrient levels, and crop health in real-time, facilitating data-driven decision-making (Ruan et al., 2019). However, the large-scale application of IoT in agriculture faces challenges, including the need for substantial investment and the technical proficiency of farmers. Addressing these challenges through innovative financing and management solutions will be crucial for the widespread adoption of IoT in agriculture. 7.3 Challenges to widespread adoption of precision agriculture Despite the promising advancements, several challenges hinder the widespread adoption of precision agriculture. One major challenge is the integration of various data sources and the effective use of this data to make informed decisions. While technologies like GPS and variable rate technology (VRT) have advanced, there is still a gap in the methods and knowledge required to utilize the vast amounts of data generated (Smidt et al., 2016). Additionally, the high initial costs and the need for technical expertise can be barriers for many farmers. Long-term studies have shown that while precision agriculture can reduce temporal yield variation and increase yield stability, the spatial yield variation remains largely unaffected, indicating the need for more refined techniques (Yost et al., 2017). 7.4 Prospects for future soybean yield improvement The future of soybean yield improvement lies in the integration of advanced technologies and a deeper understanding of crop physiology. Precision breeding, leveraging genome editing and molecular knowledge, offers a promising avenue for developing soybean varieties with enhanced yield potential (Vogel et al., 2021). Furthermore, the use of UAVs and machine learning to monitor and predict crop performance can lead to more precise and efficient farming practices (Eugenio et al., 2020; Maimaitijiang et al., 2020; Ren et al., 2023). The adoption of sensor-based nutrient and irrigation management systems has already shown significant improvements in soybean yield and resource-use efficiency, suggesting that these technologies will play a crucial role in future agricultural practices (Sachin et al., 2023a). By addressing the current challenges and continuing to innovate, the prospects for soybean yield improvement are highly promising.
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