Implementation of a Convolutional Neural Network Algorithm in Classifying Vegetable Freshness Based on Image
Abstract
The purpose of this work is to apply CNN algorithm to a real problem of vegetable freshness identification using image data. Quantitative approach was used for this study and the data source was obtained from Kaggle; it is referred to as Fresh and Stale Images of Fruits and Vegetables with 2,604 images, four categories in total. The CNN model architecture consisted of a basic organization of four successive convolutional layers with associated max-pooling layers that aimed at capturing hierarchical feature representations of the input images. This model was trained using the Adam’s optimizer for 20 iterations with the batch size of 32. Pre-processing of data included image augmentations such as scaling, rotation, flipping which improved the performance of the model. The assessment was done using Confusion Matrix approach and the results show that the proposed system achieved an accuracy of 95%, with a precision of 94%, recall of 93% and F1-score of 93%. From this it can be concluded that the CNN model proposed has achieved the objective of distinguishing fresh and non-fresh vegetables with enough precision to assist in the automation of quality control in agriculture. The conclusion that can be drawn from this study is that AI especially CNNs could be of big help in increasing accuracy and decreasing human factors in the large scale production of food.
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