• Keerthana S
• Praveena K
• Karthika K
• Jeyalakshmi V

Three novel methods for fruit image analysis and classification using MATLAB are proposed. The first method utilizes color-based segmentation in the HSV color space, isolating fruits through predefined thresholds. The second method enhances segmentation using binary masks and RGB masking, providing detailed visualizations of the segmented fruits. The third method employs a fine-tuned GoogleNet model, adapting the last layers for custom classification of a diverse dataset of fruits and vegetables, thereby improving accuracy through transfer learning. Collectively, these approaches demonstrate effective techniques for fruit segmentation and classification, highlighting MATLAB's capabilities in image processing.

Fruit Image Processing, Classification, MATLAB, HSV Color space, Binary masks, Google Net, Transfer learning.

"A Survey on Enhancing Fruit Image Analysis: Classification and Segmentation Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 2, page no.a720-a727, February-2025, Available :http://www.jetir.org/papers/JETIR2502093.pdf

"A Survey on Enhancing Fruit Image Analysis: Classification and Segmentation Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 2, page no. ppa720-a727, February-2025, Available at : http://www.jetir.org/papers/JETIR2502093.pdf