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@article{182544,
        author = {Khushi Shukla and Sayali Nannaware and Vidhi Pohankar and Amruta Hate and Sejal Shingane},
        title = {Enhancing Fruit counting solution},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {2903-2912},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182544},
        abstract = {Accurate fruit counting is essential for yield estimation, resource management, and automation in modern agriculture. This study presents a robust computer vision-based solution for counting circular-shaped fruits such as oranges, apples, and lemons in natural orchard environments. The proposed method combines image preprocessing, circular shape detection using Hough Circle Transform, and deep learning-based segmentation for improved accuracy in cluttered or overlapping scenarios. The system is capable of handling variations in lighting, occlusion, and fruit size, making it adaptable to real-world conditions. Experimental results demonstrate high accuracy and efficiency across multiple datasets, validating the potential of the solution for integration into agricultural monitoring systems and autonomous harvesting technologies.},
        keywords = {Circular fruit counting, Round fruit detection, Fruit counting solution, Fruit recognition system, Circular object detection.},
        month = {July},
        }