A Comparative Study of Multiclass Classification Using the Different Machine Learning Techniques for Fruit Species Prediction from Images
Rugved Korde1, Achal Sultane2, Vedant Mahitkar3, Shreyal Ikhar4, Achal Mokhale5, Prof. S. N. Sawalkar6
1,2,3,4,5 U.G Students, Department of Computer Science and Engineering, SIPNA COET, Amravati, Maharashtra, India.
6Professor, Department of Computer Science and Engineering, SIPNA COET, Amravati, Maharashtra, India
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Abstract - Fruits play a vital role in our healthy life and are also used for the treatment of various diseases. It also contains an enormous quantity of fibers. It is the application of machine learning that we are using in the fruit classification model. Here we have different fruit images and we have to classify them using multiple algorithms. We are using various algorithms like KNN, random forest, Naive Bayes algorithms, etc. When we are using these algorithms we need our data in numbers or we can say in the numeric format, so we have to convert our fruit image data into a numeric format, and then by applying the various algorithms we can perform the task of classification. In this paper, a machine learning-based approach is presented for classifying and identifying different fruits with a dataset that contains various images. Some images are for training and some images are for validation and for testing. Here we have to take note of one thing while we are dealing with the machine learning and deep learning task or any project we want our data in numeric format. Here we are importing various types of libraries. Food security is a very important topic of discussion in today's society, as improper handling and management of food during production, processing, or distribution have caused increased food wastage around the globe. In addition, it has become clear from statistics gotten from surveys by institutions around the world like the Food Bureau of the United States that it is necessary to increase our rate of food production to meet the needs of our rapidly growing population.
Key Words: Machine learning, image processing, prediction, classification, fruit, health, diseases
