This research concentrated on fruit image recognition. Fruit recognition in this study can be used to estimate the number of fruits that exist. Data testing is used to classify a fruit image that has been trained to recognize a variety of labels (fruit types). Until the classification process, several processes and methods are used in this research, one of which is the Gaussian filter to improve the quality of fruit image recognition. In addition, the Gabor filter is used in the feature extraction process, and the PCA technique is used in feature selection to select the best features. To classify the chosen feature, deep learning and the k-nearest neighbor (k-NN) method will be used. Furthermore, the processes used improved the accuracy of 92%, a root mean squared error (RMSE) of 0.323, a mean squared error (MSE) of 0.6278.