Legume Genomics and Genetics 2025, Vol.16, No.6, 279-287 http://cropscipublisher.com/index.php/lgg 284 through simulation software and field trials, so that resource utilization is more efficient. As long as the climate, soil and variety reactions of various places are fully considered, these density optimization and supporting measures can be extended to all soybean producing areas across the country. These research results provide a basis for formulating soybean planting strategies suitable for different regions. By scientifically arranging sowing density and agronomic management, the yield and quality of soybeans in my country can be further improved. Figure 2 Growth of single spring soybean plants at the full seed (R6) stage (Adopted from Huang et al., 2024) Image caption: S1: April 7; S2: May 7; D1: 206,800 plants/ha; D2: 308,600 plants/ha; D3: 510,200 plants/ha (Adapted from Huang et al., 2024) 8 Future Directions 8.1 Precision agriculture tools for optimizing sowing density There are many new precision agriculture equipment that are changing the way seeding density is adjusted. Automated robots, smart seed meters, and various sensor systems can accurately place seeds one by one, and can also change the seeding rate while sowing, and even automatically re-sow when missing points are found. This not only saves materials and waste, but also increases yields (Naik et al., 2016). Some battery-powered robotic seeders can also ensure that each seed is spaced uniformly and at the right depth to adapt to different crops and plot environments, while keeping labor costs to a minimum (Victor et al., 2024). 8.2 Incorporating remote sensing and modeling for field-level recommendations Remote sensing technology combined with "precision field trials" (OFPE) is increasingly used to provide farmers with spatially coordinated sowing density recommendations. By combining satellite imagery, yield response models, and field management data, the system can automatically adjust the sowing rate based on changes in different small areas, while taking into account soil differences and time dynamics, to maximize yields and returns (Maxwell and Loewen, 2024). More advanced image analysis and modeling will also allow farmers to monitor field conditions in real time and fine-tune sowing strategies at any time, further improving accuracy and effectiveness (Li and Li, 2022; Thite and Prakash, 2025).
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