As the global population continues to grow, climate change intensifies, and agricultural resources become increasingly scarce, traditional agricultural production models are facing unprecedented challenges. It is predicted that by 2050, global food production will need to increase by 70% to meet demand. Against this backdrop, “Agriculture 4.0” has gradually emerged, with embedded technologies such as the Internet of Things (IoT), artificial intelligence (AI), unmanned aerial vehicles (UAVs), and robotics widely applied in precision agriculture to improve production efficiency and sustainability.

In the field of modern agricultural machinery, pneumatic conveying seed-discharge systems have been widely adopted due to their compact size, high seed-discharge efficiency, and strong adaptability, which are particularly aligned with the current development trend of high-speed mechanized operations.

Recent survey delivers the first systematic benchmark of TSP solvers spanning end-to-end deep learners, hybrid methods and brand-new LLM-based hybrids, revealing that hybrids give best-in-class routes in record time.

Meet ncRNA2MetS v2.0, the largest experimentally verified repository linking 1,505 ncRNAs to metabolic syndrome and its five core disorders, offered through an intuitive eight-module web portal.

Researchers introduce a group-driven initialization that fuses search history with graph modularity, boosting state-of-the-art local solvers for k-quasi-clique and k-plex without altering their search routines.

CRISPR-editing OsFAD2-1 boosts oleic acid and stabilizes rice bran oil. Edited lines maintain key agronomic traits of breeding-ready germplasm.