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

Abstract

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.