Robotic Sensor Identifies Shapes of Crops Using Sound

Researchers from Carnegie Mellon University’s Robotics Institute (RI) invented a tool called SonicBoom that can find crops like apples based on the sound they make. The research team used an array of six contact microphones placed inside a piece of PVC pipe. When the pipe touches an object, such as a tree branch, the microphones detect the resulting vibration. By analyzing the differences in the sound waves, the researchers were able to triangulate where the contact took place. SonicBoom can localize contacts with a precision between 0.43 and 2.2 centimeters.

The PVC pipe protects the contact microphones from damage. It also gives the appearance of a microphone boom, inspiring the name SonicBoom. Ultimately, the microphones could be installed inside a robot arm. The researchers used a data-driven machine learning module to develop the ability to map the signals from the microphones. To do so, they collected audio data from 18,000 contacts between the sensor and a wooden rod. Using the audio data, SonicBoom determines the location of hard or rigid objects. Changing its configuration should enable it to also sense less rigid objects, such as soft fruits and vegetables.

More information:

https://www.cmu.edu/news/stories/archives/2025/august/new-robotic-agricultural-sensor-could-revolutionize-farming

Fully Autonomous Farm

Farms are moving toward full autonomy thanks to advances in AI, robotics, and digital tools. High costs and the lack of broadband Internet in rural areas, however, pose major obstacles.

Technologies being deployed on farms include autonomous tractors, robots and drones capable of picking fragile fruits, sensors that provide soil analysis, virtual fences to rein in livestock, and remote sensing and image analytics tools.

More information:

https://www.wsj.com/tech/autonomous-farming-ai-95657bd1

Laser-Guided Robot Farmers

Agricultural robots are inching closer to becoming practical farm hands, thanks to a new navigation system developed by researchers in Japan. These robots can now autonomously travel between high-bed cultivation rows, like those used for growing strawberries, without relying on expensive infrastructure or perfect positioning systems. Rather than expensive GPS systems or having to install special markers in fields, these robots use a straightforward approach that mimics how humans navigate. They keep a consistent distance from the crop beds while moving, adjusting as they go. When the robot needs to move from one general area to another, it uses waypoint navigation to reach predefined spots. Once it arrives near crop rows, it switches to cultivation bed navigation, where it uses a laser scanner to maintain a specific distance and orientation to the crop bed.

This hybrid approach helps robots move accurately through farm rows, even in places where GPS and other positioning tools don’t work well. Between rows of strawberries, a robot’s sensors detect limited distinctive features, making it hard to know exactly where it is. By using the cultivation beds themselves as guides, the robot can move accurately without perfect self-localization. During testing in both simulated and real-world environments, the system kept the robot within 0.05 meters (about 2 inches) of its target distance from cultivation beds and maintained its orientation within 5 degrees which is impressive precision for agriculture. Testing in virtual environments helped them refine the robot’s specifications and navigation algorithms without wasting time and resources in real fields. This new system was much more efficient than older navigation methods.

More information:

https://studyfinds.org/robot-farmers-farm-labor-force/