Fruit Quality Classification

This research will implement an Agriculture 4.0 solution for automated fruit quality assessment through computer vision and machine learning techniques. The system will process digital images to evaluate fruit characteristics and assign quality grades based on predetermined parameters.

The architecture will leverage transfer learning to enable rapid adaptation for different fruit varieties without extensive retraining. Image preprocessing, feature extraction, and classification modules will be developed to ensure consistent quality assessment across varying operational conditions. The implementation will aim to minimize human intervention in the grading process while maintaining classification accuracy and processing efficiency.

This contribution to Agriculture 4.0 will address the increasing demand for automated quality control in fruit processing operations, potentially reducing labor dependencies and standardizing grading procedures. The system will be designed to integrate with existing post-harvest processing lines, enabling practical implementation in commercial settings.