Aim: The objective of this study is to implement deep learning technique based on innovative classification using convolutional neural networks algorithm to improve the detection of disease in fresh fruit compared with the Decision Tree algorithm to increase accuracy, precision, and sensitivity. Materials and Methods: A total of 2500 samples were collected from kaggle’s various fruit images. A training dataset of 1750 (70%) and a test dataset of 750 (30%) were generated from these samples. To measure the efficiency of the Convolutional neural network algorithm, accuracy, precision, and sensitivity values were measured. For each sample, 10 samples were collected for analysis. The significance p