Authors : Mohammad Bilal M; Dr. Shivandappa; Sanju H K; Dr.Narendra Kumar S; Vignesh Kumar Kaipa

Volume/Issue : Volume 9 - 2024, Issue 9 - September

Google Scholar : https://tinyurl.com/2cnevjby

Scribd : https://tinyurl.com/4xv6ap9z

DOI : https://doi.org/10.38124/ijisrt/IJISRT24SEP163

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This paper presents a system developed for the automated classification of different mango varieties using Convolutional Neural Networks (CNNs). The model was trained on an image dataset containing labeled mango varieties, which was augmented to enhance robustness. The CNN architecture comprises convolutional layers, pooling layers, and fully connected layers, optimized using TensorFlow. The system achieved satisfactory accuracy on both training and validation datasets. Evaluation was conducted using confusion matrices and training curves. The proposed system can classify mango images in real- time, providing predictions with confidence scores. The results demonstrate the potential of deep learning in automating fruit classification tasks, offering significant benefits for agricultural and retail sectors by improving efficiency and accuracy.

Keywords : Mango Classification, Convolutional Neural Networks, Deep Learning, Image Processing, Tensorflow.

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