Smart Agricultural Automation through Fruit Recognition System |
Author(s): |
| Mr. Shrinivas Gopal Kulkarni , TPCTs College of Engineering, Dharashiv; Prof S. A. Gaikwad, TPCTs College of Engineering, Dharashiv |
Keywords: |
| Fruit Classification, Computer Vision, Feature Extraction, AdaBoost Classifier, Machine Learning, Image Processing |
Abstract |
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Fruit classification is one of the significant applications of computer vision and machine learning. Due to the wide variation in fruit color, texture, and shape, accurate recognition is a challenging task. This study presents an automatic fruit classification system using image preprocessing, feature extraction, and the AdaBoost classifier. The proposed approach extracts features such as color histograms, edge descriptors, and Haar-like patterns to train the model. A dataset consisting of 120 fruit images from five categories was used for experimentation. The model achieved an accuracy of approximately 55%, indicating that ensemble algorithms provide a baseline but more robust methods are required for higher accuracy. This work demonstrates the potential of machine learning in agricultural automation and provides directions for further improvement. |
Other Details |
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Paper ID: IJSRDV13I70026 Published in: Volume : 13, Issue : 7 Publication Date: 01/10/2025 Page(s): 22-23 |
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