Analysis, Design and Implementation of a Ripe Mango Detection Program in Burkina Faso
- Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST) , 588 : 181-189
Résumé
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.
Mots-clés
YOLOv5, object detection, fruit counting, agricultural image analysis, Burkina Faso