Synergistic research on planter performance optimization and green low-carbon agricultural transformation under climate risk

Introduction Addressing the dual challenges of climate change and sustainable food production, this study proposed an integrated framework that combined planter performance optimization with green, low-carbon agricultural transformation. While traditional planting strategies focused on parameters like seed depth, speed, and spacing, they often neglected environmental sustainability and adaptability to climate variability. Methods To bridge this gap, we introduced the Adaptive Precision Planter Optimization Model (APPOM), which leveraged real-time environmental sensing, machine learning, and multi-objective optimization to dynamically adjust key planting parameters. Our approach also incorporated green technologies, including electric-powered planters and carbon-sequestration soil practices, to reduce the ecological footprint of agricultural operations. Results Experimental results validated that APPOM significantly improved planting accuracy, enhanced resource efficiency, and reduced carbon emissions across diverse soil and climate conditions. Furthermore, we presented the Real-Time Adaptive Planter Optimization (RAPO) strategy, which enabled context-aware decision-making and continuous optimization under field variability. Discussion The findings underscored the potential of intelligent, eco-friendly planting systems to foster climate-resilient agriculture. However, challenges such as cost barriers and deployment scalability remained. Future research should aim to enhance affordability and accessibility, particularly for smallholder farmers, and expand the framework to a broader range of crops and regions.