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.

Selective catalytic reduction (SCR) is now an established NO_xremoval technology for industrial flue gas as well as for diesel engine exhaust gas. However, it is still a big challenge to develop a novel low-temperature catalyst for NH₃-SCR of NO_x, especially at a temperature below 200°C. In the past few years, many studies have demonstrated the potential of iron (Fe)-based catalysts as low-temperature catalysts for NH₃-SCR of NO_x. Herein, we summarize the recent progress and performance of Fe-based catalysts for low-temperature NH₃-SCR of NO_x. Catalysts are divided into three categories: single Fe_xO_y, Fe-based multimetal oxide, and Fe-based multimetal oxide with support catalysts. The catalytic activity and selectivity of Fe-based catalysts are systematically analyzed and summarized in light of some key factors such as activation energy, specific surface area, morphology, crystallinity, preparation method and precursor, acid sites, calcination temperature, other metal dopant/substitute, and redox property of catalysts. In addition, H₂O/SO₂tolerance and the NH₃-SCR reaction mechanism over Fe-based catalysts, including Eley-Rideal and Langmuir-Hinshelwood mechanism, are emphasized. Lastly, the perspectives and future research directions of low-temperature NH₃-SCR of NO_xare also proposed.

The current work applies L9 (3³) Orthogonal Array (OA) of Taguchi design to obtain an optimum combination of nature of fuel, speed of engine, and load for DI- CI diesel engine that is alternatively fueled with waste cooking oil (WCO) based pure biodiesel (B100) & 20% blend of biodiesel with neat diesel (B20) with an objective to achieve maximum reduction in smoke and NO_x emissions and to enhance the output parameters like engine’s heat release, cylinder pressure and brake power with the conceivable dwindling of brake specific fuel consumption of the engine at various load conditions. The optimal input parameters (nature of fuel as B100, speed as 2300 rpm and load as 100%) were specified by the grey relational analysis in such a way that the preferred output results are successfully accomplished. Furthermore, analysis of variance (ANOVA) technique showed that the nature of fuel is the major leading factor with 44.28% impact on the output parameters. The experimental and ANN simulated results at the predicted optimal combination ensured the significant improvements of output response factors, thus validating the use of Grey-Taguchi method in reducing emissions and improving combustion and performance simultaneously.