Integrating AI into Agriculture: Practical Innovations & Commercial Paths
1. Collaborative System: “Cloud Brain + Vehicle Brain”
Lanjiang Technology focuses on building an architecture-level AI ecosystem for AI agriculture. It features a “Cloud Brain + Vehicle Brain” collaborative system. The vehicle uses multi-modal sensing devices for environment perception and data collection. It also conducts on-device AI analysis to support smart farming. The cloud centrally processes data and trains AI agriculture models. It gains capabilities in pest identification, risk early warning and agronomic decision-making. Cloud-edge collaboration enables Lanjiang’s agricultural robots to learn continuously. They upgrade from “executing commands” to “making intelligent decisions.”
2. Breakthrough in L4 Agricultural Autonomous Driving
Focusing on closed, low-speed orchard scenarios, Lanjiang developed its own L4 agricultural autonomous driving system. This system breaks reliance on high-precision maps and continuous satellite signals in smart farming. Furthermore, it achieves mapless navigation and autonomous positioning for AI agriculture. The system combines RTK, vision, inertial navigation and millimeter-wave radar. This enables centimeter-level trajectory control and dynamic path planning. It also supports multi-machine collaborative operations to boost AI agriculture efficiency. Consequently, agriculture becomes an important scene for commercial use of L4 agricultural autonomous driving.
3. In-depth Integration of AI and Agronomic Decision-Making
Lanjiang Technology embeds agronomic models into its AI agriculture algorithm system. For example, in precision spraying for smart farming, it adjusts spray volume based on crown density and moving speed. It also realizes one-sided spraying and intelligent start-stop to enhance AI agriculture efficiency. In precision weeding, it identifies seedlings and weeds through semantic segmentation. Moreover, it achieves targeted spraying and reduces pesticide use in smart farming. By building an orchard large model, it gradually realizes “tree-by-tree decision-making” in AI agriculture. This promotes agriculture from experience-driven to data-driven smart farming.
4. Selection of Commercializable Agricultural AI Scenarios
Under the rapid development of large models, domestic AI agriculture capabilities are improving. However, large-scale use of general intelligence still takes time. Agriculture has advantages for Lanjiang’s AI agriculture: closed environments and standardized tasks. It also has measurable investment returns in smart farming. Therefore, it is more likely to first realize a workable AI agriculture business model. Lanjiang takes orchard plant protection as the entry point for AI agriculture. In addition, it achieves technology landing and large-scale replication in smart farming. This explores a clear industrialization path for AI agriculture.
Conclusion
Overall, Lanjiang Technology’s “AI + Agriculture” is not just technology superposition. In fact, this effort is a systematic smart project built around AI agriculture scenarios. Besides, it achieves the overall upgrade from equipment automation to decision intelligence in smart farming. Additionally, it gains platform-based operational abilities for Lanjiang’s AI agriculture and L4 agricultural autonomous driving.
