Machine Vision-Based Apple Detection and Counting
Machine Vision-Based Apple Detection and Counting
Designed and developed an integrated computer vision system for automated apple detection and counting by combining traditional image processing techniques with machine learning classifiers. The solution utilized colour-space segmentation, feature extraction, and supervised learning models to accurately identify apples under varying lighting conditions and complex orchard environments, with validation across multiple benchmark datasets.