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Automated Mango Classification Using Convolutional Neural Networks (CNN)

Year: 2024

Type: article

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.

Source: International Journal of Innovative Science and Research Technology (IJISRT)

DOI: 10.38124/ijisrt/ijisrt24sep163

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

Institution: R.V. College of Engineering

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Zero hunger

Open Access status: diamond

OpenAlex ID: W4402615252