Automation in agricultural machinery, underpinned by the integration of advanced technologies, is revolutionizing sustainable farming practices. Key enabling technologies include multi-source positioning fusion (e.g., RTK-GNSS/LiDAR), intelligent perception systems utilizing multispectral imaging and deep learning algorithms, adaptive control through modular robotic systems and bio-inspired algorithms, and AI-driven data analytics for resource optimization. These technological advancements manifest in significant applications: autonomous field machinery achieving lateral navigation errors below 6 cm, UAVs enabling targeted agrochemical application, reducing pesticide usage by 40%, and smart greenhouses regulating microclimates with ±0.1 °C precision. Collectively, these innovations enhance productivity, optimize resource utilization (water, fertilizers, energy), and mitigate critical labor shortages. However, persistent challenges include technological heterogeneity across diverse agricultural environments, high implementation costs, limitations in adaptability to dynamic field conditions, and adoption barriers, particularly in developing regions. Future progress necessitates prioritizing the development of lightweight edge computing solutions, multi-energy complementary systems (integrating solar, wind, hydropower), distributed collaborative control frameworks, and AI-optimized swarm operations. To democratize these technologies globally, this review synthesizes the evolution of technology and interdisciplinary synergies, concluding with prioritized strategies for advancing agricultural intelligence to align with the Sustainable Development Goals (SDGs) for zero hunger and responsible production.

Soil pH critically regulates microbial community structure and activity, thereby influencing carbon transformation processes in terrestrial ecosystems. However, the mechanisms underlying pH-mediated shifts in microbial metabolic functions and cellulose-degrading functional genes remain poorly understood. This study investigated the responses of bacterial communities, metabolic profiles, and the abundance of cellobiohydrolase I (cbhI) and glycoside hydrolase family 48 (GH48) genes to varying pH levels in fluvo-aquic and red soils. High-throughput sequencing, PICRUSt-based metabolic prediction, and quantitative PCR were employed to analyze microbial composition, functional traits, and gene dynamics. Network analysis clarified linkages between functional genes, pathways, and taxa. The results revealed that elevated pH significantly increased CO₂ emissions and dissolved organic carbon (DOC) content in both soils. Dominant taxa, including Alphaproteobacteria, Bacteroidetes, Xanthomonadaceae, and Mycoplasma, exhibited pH-dependent enrichment. Metabolic predictions indicated that pH positively influenced genes linked to biodegradation and xenobiotic metabolism in fluvo-aquic soil but suppressed energy-metabolism-related genes. Contrastingly, in red soil, cbhI and GH48 gene abundance declined with rising pH, suggesting that acidic conditions favor cellulolytic activity. Network analysis identified strong positive correlations between CO₂ emissions and Caulobacteraceae, while cbhI and GH48 genes were closely associated with taxa such as Xanthomonadaceae, Comamonadaceae, and Micromonosporaceae, which drive organic matter decomposition. These findings underscore pH as a pivotal regulator of microbial community structure and functional gene expression, with soil-specific responses highlighting the need for tailored strategies to optimize carbon cycling and sequestration in agricultural ecosystems.

Bearingless interior permanent magnet synchronous motors (BIPMSMs) have received considerable attention in recent research due to their advantages of high speed, high power density, and absence of mechanical wear. In order to improve the torque and suspension performance of the BIPMSM, an optimization design method of BIPMSM is proposed in this paper based on sensitivity analysis, response surface fitting, and the multi-objective whale optimization algorithm (MOWOA). Firstly, the structure and operation principle of the BIPMSM are introduced. Secondly, significant variables are extracted based on sensitivity analysis. Then, regression equations of the significant variables and optimization objectives are fitted by the response surface method, and global optimization is performed with MOWOA. Finally, the motor performance before and after optimization is compared. The results demonstrate that the proposed multi-objective optimization design scheme can significantly improve the performance of the BIPMSM and effectively shorten the design cycle.