The labour-intensive work of harvesting delicate blackberries by hand is a must, but the development of advanced technologies by Mississippi State scientists could help automate the tedious process.

Many agricultural crops are picked quickly by machines, and MSU Assistant Professor Xin Zhang, of the Department of Agricultural and Biological Engineering, is working with a university team to do the same for ripe blackberries—taking this high-value specialty crop from special handling to robotic harvesting.

Zhang and her team are developing a blackberry detection and localization system, the “eyes” and “brain” of a robotic harvester system powered by an innovative, artificial intelligence-driven deep learning approach. 

Zhang is co-principal investigator on a $1 million multi-institutional effort funded by the USDA National Institute of Food and Agriculture National Robotics Initiative 3.0 (NRI-3.0) program in collaboration with the National Science Foundation. 

As the MSU team develops this critical component of the automated harvester, partners at Georgia Institute of Technology are working on a soft-touch robotic arm and gripper and a bipedal mobile platform to work hand-in-glove with the MSU-trained perception system. The prototype gripper is equipped with sensors located at the ends—like tiny fingertips—allowing it to grasp and pick the berry without squeezing and damaging it. University of Arkansas scientists are focused on post-harvesting fruit analysis.

The perception system designed by Zhang and her team is powered by YOLOv8 (You Only Look Once, version 8), a vision-based object detection model that identifies and locates objects of interest—in this case, ripe blackberries—quickly and accurately. This kind of technology is powerful enough to support robots, surveillance systems and self-driving cars.

Source:  Mississippi State University August 1, 2024 news release