computer-vision

Designed and developed an integrated computer vision system for automated apple detection and counting by combining traditional image processing techniques with supervised machine learning algorithms. The solution was built to improve the accuracy and efficiency of fruit detection in orchard environments while reducing the need for manual counting.

Implemented image preprocessing, colour-space segmentation, and feature extraction techniques to accurately identify apples under varying lighting conditions, occlusions, and complex backgrounds. The system was designed to handle challenging real-world scenarios and ensure consistent detection performance across different orchard settings.

Evaluated multiple supervised learning classifiers using benchmark datasets and conducted comparative performance analysis based on key evaluation metrics such as accuracy, precision, recall, and F1-score. The results demonstrated the robustness and reliability of the proposed approach for automated fruit detection and counting in precision agriculture applications.