The Brain Behind Precision Harvesting: JHCTECH BRAV-7135 Edge Computing Solution Empowers a New Era of Smart Agriculture
Amid the structural challenges of an aging population and seasonal labor shortages in global agriculture, intelligent harvesting robots are becoming a key force driving industrial upgrading. However, traditional mechanization alone is insufficient to handle the complex and dynamic conditions of field operations. True intelligence requires powerful edge computing capabilities as its foundation.
The JHCTECH BRAV-7135 high-performance edge controller is a high-performance computing platform capable of meeting the stringent requirements of intelligent harvesting robots. With its powerful computing capabilities, rich interfaces, and industrial-grade reliability, the BRAV-7135 provides intelligent harvesting systems with a complete edge computing solution, from environmental perception and decision-making to precise control.
The Challenges of Smart Agriculture: More Than Just Robots, It Needs a Smart "Brain"
Global agriculture is facing three major pain points:
✔ Harvesting relies on manual labor: High cost, low efficiency, and difficulty in standardization
✔ Complex field environments: Variable lighting, foliage obstruction, and uneven terrain
✔ High real-time requirements: Detection, positioning, and harvesting must respond within milliseconds
Traditional cloud-based solutions often suffer from high latency and strong network dependency. In contrast, edge computing is rapidly emerging as the key technology path for enabling intelligent agricultural robotics.
BRAV-7135：An Edge Computing Platform Designed for Smart Farming
Hardware level:
The perception system utilizes dual RGB cameras via USB or Ethernet to achieve depth sensing and accurately measure fruit location; a multispectral camera detects ripeness and defects for selective harvesting; and a wide-angle camera and LiDAR are used for environmental mapping and path planning, enabling obstacle avoidance and terrain modeling.
The execution system integrates a 6-axis collaborative robotic arm via CAN; and an end effector with adaptive grippers or vacuum suction cups via RS-485 and CAN, along with force sensors.
Auxiliary systems include an emergency stop switch and collision avoidance sensors.
Software level:
We assisted clients in porting various algorithm libraries, including AI stacks for vision and inference. The ROS2 integrated into the robot system enables efficient and decoupled collaboration between perception, planning, and control modules. It also supports the management of robot models, coordinate transformations (TF), and point cloud data. On the perception side, an integrated SDK is used to build multi-stream video AI processing pipelines, allowing efficient handling of visual data from multiple cameras.
In addition to intelligent path-planning algorithms that ensure coordinated motion between the robotic arm and the mobile chassis, the system integrates dynamic target recognition algorithms to adapt to variations in lighting conditions and foliage occlusion. It also incorporates adaptive grasping algorithms that adjust gripping force based on the shape and firmness of the fruit.
The system adopts a coordinated hardware–software architecture, leveraging LiDAR-based mapping and SLAM for centimeter-level localization, supported by RTK-GPS for global positioning. Visual re-localization is further applied to correct cumulative errors. During the target recognition stage, a wide-angle camera performs initial scanning to identify fruit-bearing areas, while multispectral analysis evaluates ripeness to ensure that only qualified fruit is harvested. In the harvesting stage, algorithms calculate the optimal picking path, while adaptive grasping dynamically adjusts gripping force. Inertial measurement is used to ensure stability throughout the harvesting process. After harvesting, the system automatically sorts and places the fruit based on ripeness levels, while simultaneously performing yield statistics and preliminary quality inspection. The entire workflow also supports autonomous charging and anomaly reporting.
The intelligent harvesting robot equipped with the BRAV-7135 reduces labor costs and improves harvesting efficiency while minimizing waste, making data such as yield forecasts and growth analysis more manageable.
Learn more about BRAV-7135 series