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.

With the ongoing advancement of global agricultural modernization, intelligent technologies have gained significant attention in agricultural production—particularly in the field of intelligent orchard sprayers, where notable progress has been achieved. Intelligent orchard sprayers, equipped with precise sensing and control systems, enable targeted spraying. This enhances the efficiency of crop health management, reduces pesticide usage, minimizes environmental pollution, and supports the development of precision agriculture. This review focuses on three core modules of intelligent sprayer technology: perception and intelligent control, spray deposition and drift control, and autonomous navigation with system integration. By addressing key areas such as sensor technologies, object detection algorithms, and real-time control strategies, this review explores current challenges and future directions for intelligent orchard sprayer technology. It also discusses existing technical bottlenecks and obstacles to large-scale adoption. Finally, this review highlights the pivotal role of intelligent orchard sprayer technology in enhancing crop management efficiency, improving environmental sustainability, and facilitating the transformation of agricultural production systems.

In the process of applying plant protection sprays, the atomisation process of complex pesticide components such as emulsion pesticides is different from that of water. Indeed, emulsion is often used as an additive to spray to reduce drift. Therefore, this study investigated the different morphological characteristics that occur between emulsions and water during atomisation at different pressures through visualisation experiments and interpreting the formation of structural differences between the two fragmentation mechanisms. The effect of liquid sheet structure on droplet size distribution was analysed in three-dimensional space, not only from one spatial perspective, but how it alters the morphological structures of liquid sheet leading to different potential droplet drift characteristics. It was found that the smaller the liquid sheet disturbance, the more concentrated the droplet size distribution, the more intense the liquid sheet disturbance, the more dispersed the droplet size distribution. The addition of 0.02% emulsion significantly reduced the proportion of V₁₀₀ (the ratio of volume with drops smaller than 100 μm to the total volume of all droplets) from 21.33% to 10.24%, and the higher the emulsion concentration, the smaller the V₁₀₀. The ability of the emulsion to increase V₄₀₀ decreased with increasing pressure.

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.

Upscaling in situ soil moisture observations (ISMO) to multiscale pixel estimations with kriging is a key step in the comprehensive usage of ISMO and remote sensing (RS) soil moisture data. Scale effects occur and introduce uncertainties during upscaling processes because of spatial heterogeneity and the kriging method. A nested hierarchical scale series was established at the field level, and upscaled estimations at each scale were obtained by block kriging (BK) to illustrate multiscale ISMO upscaling processes. Those uncertainties were described with the results of comparison analysis against RS data, statistical analysis, and spatial trend surface analysis on multiscale estimations and were explained from the spatial heterogeneity perspective with a semivariogram analysis on ISMO. The results show that uncertainties exist and vary in multiscale upscaling processes, and the range of the empirical semivariogram could indicate scale effects. When the target scale is shorter than the range, BK maintains similar scale effects and global trends during upscaling processes, and the direct pixel estimation by BK is relatively close to the average of nested pixel estimations. This has great implications for understanding the kriging method in similar works.