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.

Biomass gasification represents a pivotal technology for sustainable energy and chemical production, yet its efficiency and product quality are critically dependent on catalyst performance. This comprehensive review systematically synthesizes recent advancements in catalyst design, mechanistic insights, and process integration in biomass gasification. Firstly, it details the development and performance of catalysts in diverse categories, including metal-based catalysts, Ca-based catalysts, natural mineral catalysts, composite/supported catalysts, and emerging waste-derived catalysts. Secondly, this review delves into the fundamental catalytic reaction mechanisms governing key processes such as tar cracking/reforming, water–gas shift, and methane reforming. It further explores sophisticated strategies for catalyst structure optimization, focusing on pore structure/surface area control, strong metal–support interactions (SMSIs), alloying effects, nanodispersion, and crystal phase design. The critical challenges of catalyst deactivation mechanisms and the corresponding activation, regeneration strategies, and post-regeneration performance evaluation are thoroughly discussed. Thirdly, this review addresses the crucial integration of zero CO₂ emission concepts, covering in situ CO₂ adsorption/conversion, carbon capture and storage (CCS) integration, catalytic CO₂ reduction/valorization, multi-energy system synergy, and environmental impact/life cycle analysis (LCA). By synthesizing cutting-edge research, this review identifies key knowledge gaps and outlines future research directions towards designing robust, cost-effective, and environmentally benign catalysts for next-generation, carbon-neutral biomass gasification systems.

As a key agricultural machine, the intelligent development of combine harvesters is vital for boosting operational efficiency, cutting resource use, and promoting sustainable agriculture. This review deeply analyzes the technological progress and application effects of intelligent combine harvester systems in five areas: autonomous navigation, adaptive operation control, hybrid power, fault monitoring and diagnosis, and intelligent cleaning. In autonomous navigation, multi-sensor fusion enables centimeter level precision in complex environments. For instance, YTO’s unmanned harvester in Henan’s wheat-growing area operated for eight hours straight, cutting loss rates by 15%. Adaptive operation control systems, by monitoring crop and machine-running data in real time and dynamically optimizing parameters, have reduced rice - harvesting grain loss from 3.5% to 1.2% in traditional models. Hybrid power systems lower fuel consumption by 42.4% per unit area compared to diesel powered ones and cut annual CO2 emissions by 8.6 tons. In fault monitoring and diagnosis, multi-modal sensor networks and data fusion strategies enhance monitoring accuracy, while AI driven fault models boost diagnostic precision. The IPSO-SVM fault diagnosis algorithm, for example, has a 97.96% accuracy rate. Intelligent cleaning systems, using multi-sensor perception, smart decision making algorithms, and precise actuators, have transitioned from experience based to data driven approaches, reducing loss rates by 15%.This review comprehensively sums up the current state, key technologies, application cases, and future trends of combine harvester intelligent systems. It offers a full reference for global agricultural machinery intelligence and promotes the automation, precision, and efficiency of agricultural 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.

Since the late 1980s, additive manufacturing (AM) has emerged as a prominent fabrication technology, experiencing significant advancements over the past decades. Even with its rapid development, this technology still faces many manufacturing challenges. Driven by the development of AM, 5D printing evolved from conventional 3D printing by incorporating five controlled degrees of freedom (three translational and two rotational axes) to enable non-planar deposition, curved-layer fabrication, and improved control of deposition orientation. This review critically analyses and summarizes 5D printing technology, detailing its development, material innovations, applications, and future outlook. The study starts with the evolution of printing from 1D printing, through 2D printing, to 3D printing, detailing its limitations and the paradigm shift to 5D printing. Further insights are also discussed regarding material selection, which plays a crucial role in determining the functionality and performance of 5D printing. Moreover, various applications of 5D printing across the aerospace, automotive, biomedical, jewelry, and food industries have been discussed, highlighting its potential for lightweight, high-strength, and tailored components. Furthermore, a detailed SWOT analysis of 5D printing has been presented to evaluate the strength, weaknesses, opportunities and threats of the technology, providing a strategic perspective of 5D printing in the industry. Lastly, future prospects for multi-material integration, AI-optimized printing and hybrid manufacturing methods are discussed.