Cotton Genomics and Genetics 2025, Vol.16, No.2, 48-56 http://cropscipublisher.com/index.php/cgg 52 environmental constraints. The lessons learned from Xinjiang's experience highlight the importance of integrating advanced technologies and tailored management practices to overcome regional agricultural challenges and enhance productivity. Figure 2 Testing performance [R2 (A, B), RMSE (C, D) and rRMSE (E, F)] of cotton yield prediction only with remote sensing variables and combined with climate variables using the LSTM model for the whole growing season during 2012-2018 and 2019, respectively (Adopted from Lang et al., 2023) 7 Future Directions and Recommendations 7.1 Integration of AI, robotics, and automation The integration of AI, robotics, and automation in precision agriculture is pivotal for maximizing cotton yield. AI technologies, such as machine learning and computer vision, are transforming traditional farming by enabling real-time data analysis and decision-making, which enhances efficiency and sustainability (Hoque and Padhiary, 2024; Padhiary et al., 2024). Robotics, including autonomous tractors and drones, facilitate precise operations like planting, monitoring, and harvesting, reducing labor costs and increasing operational efficiency (Agrawal and Arafat, 2024). Future advancements should focus on developing energy-efficient AI models and improving sensor technologies to overcome current limitations such as high operational costs and technical complexity. 7.2 Policies and support for technology adoption To fully realize the potential of precision agriculture technologies, supportive policies and infrastructure are essential. Governments and private sectors must collaborate to provide training, infrastructure, and region-specific solutions for farmers (Yousafzai et al., 2024). Policy support can address challenges such as high implementation
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