The analysis, concept, and implementation of a computer program capable of detecting ripe mangoes are at the core of this study. Traditional methods are often faced with calibration errors. Recently, deep learning has shown promising performance in visually guided agricultural applications. Faced with these constraints, it is necessary to establish an automatic system for robust and efficient detection of mangoes in orchards. In this study, a fast implementation system of a mango detector, distinguishing between ripe and unripe mangoes based on deep learning using the YOLOv5 algorithm, was developed. From a simple photo, the algorithm detects and counts the number of mangoes on a tree. This artificial intelligence system (deep neural network) was trained on a dataset of over 500 annotated mango images. Experimental results show that the algorithm achieves 98% precision, 98% recall, and an F1-score of 98%. This satisfactory precision in mango detection offers significant advantages in terms of efficiency and accuracy compared to traditional methods. However, it should be noted that our system has certain limitations. Nevertheless, our study demonstrates promising results in the field of ripe mango detection.