Team Ag-Botics has integrated an AI-driven classifier and detection model with the Amiga robot to automate grape/stone fruit cluster detection and counting for precise yield estimation. By leveraging real-time data, GPS navigation, and user-friendly graphical user interface, the solution enhances productivity, streamlines farm management, and promotes sustainable grape farming practices.

To address overlapping clusters and dense foliage, depth data from stereo imaging incorporated on the Amiga bot will help improve detection accuracy. A dedicated user-friendly interface will be developed to visualize real-time data, such as cluster counts and estimated yields, providing actionable insights to farmers.

Grape and stone fruit farmers need accurate fruit yield estimates so that they can optimize harvesting schedules and allocate resources efficiently. Current methods of manual counting are labor intensive and often inaccurate, with errors in yield estimation exceeding 20%, leading to resource waste and missed harvest opportunities.