1. Core values and definitions
An unmanned agricultural vehicle is an intelligent equipment that utilizes autonomous driving technology, AI intelligent decision-making, and Internet of Things technology to independently or collaboratively complete various agricultural production tasks. Its core value lies in:
Cost reduction and efficiency improvement: Reduce dependence on manpower, enable 24-hour uninterrupted operation, and enhance operational accuracy and efficiency.
Precision management: Based on data, achieve precise operations for every plant and every square meter of land, saving water, fertilizer, pesticides, and seeds.
Sustainable development: Reduce the abuse of chemical fertilizers and pesticides, protect soil structure, and promote ecological agriculture.
Addressing labor shortage: tackling the global challenge of aging agricultural population and labor scarcity.
II. System Architecture and Key Technologies
A complete unmanned agricultural vehicle system typically consists of three major components:
1. Perception layer - "Eyes and ears"
Multi-sensor fusion: Vision system: RGB cameras are used for object recognition and lane tracking. Depth vision: LiDAR is used for real-time 3D environment modeling and obstacle avoidance; millimeter-wave radar is used to detect moving objects at a distance. Positioning system: GNSS-RTK provides centimeter-level high-precision positioning, which is the foundation for achieving precise operations. Environmental sensors: soil moisture, nitrogen content sensors, multispectral cameras, etc., are used to collect crop and soil data.
2. Decision-making level - "brain"
Core algorithms: Path planning algorithm: Plan the optimal path based on the shape of the plot and the type of operation (plowing, sowing, managing, harvesting) to avoid duplication and omissions. SLAM technology: Achieve simultaneous localization and mapping in GPS-free environments (such as greenhouses). AI model: Based on computer vision and deep learning, identify weeds, pests, and diseases, as well as the maturity of fruits, and make precise decisions for spraying or harvesting.
Cloud Platform and Digital Twin: Cloud Brain: Process massive data in the cloud, train and optimize AI models, and manage fleet scheduling. Digital Twin: Map physical farmland in virtual space for simulating operations, predicting yields, and optimizing decisions.
3. Executive level - "hands and feet"
Chassis and drive: High-precision steering control, electric or hybrid chassis, adaptable to complex terrains.
Modularized machinery: Different agricultural tools that can be quickly changed are the key to achieving multifunctionality. Cultivation module: Precise plowing and rotary tilling. Seeding module: Precise spot sowing and strip sowing. Management module: Precise variable rate fertilization, weeding, and spraying. Harvesting module: Intelligent identification and harvesting of fruits (such as tomatoes and strawberries).
Precision control system: Controls the robotic arm, nozzle switch, seeding amount, etc.