Proven Projects

Challenge

Konjac tubers have irregular shapes with frequent occlusion, making size and weight estimation inaccurate with conventional methods.

Solution

Developed a size and weight estimation method that handles occlusion, working even for partially hidden tubers.

Impact

Estimates size and weight reliably even for overlapping tubers, including hidden parts.

Challenge

Grape grading relies on expert visual assessment, leading to inconsistent standards and labor shortages.

Solution

Developed a deep learning grading system fusing multi-view imaging with IoT sensor data.

Impact

Fuses multi-view imaging and IoT sensing for stable grading without relying on manual labor.

Challenge

Grape berry thinning relies on skilled farmers' experience and intuition, making it difficult for newcomers and unskilled workers to learn.

Solution

Developed a system combining deep learning berry detection with AR glasses to display real-time thinning guidance.

Impact

Helps unskilled workers thin berries at a quality approaching that of experts.

Challenge

Grape berry thinning is highly manual labor, with severe workforce shortages and physical burden demanding automation.

Solution

Developed an automated thinning system combining berry detection AI with a robotic arm, validated in both indoor and field environments.

Impact

Demonstrated automated berry thinning in the field using berry-detection AI and a robotic arm.