Fruit identification and ripeness detection system based on YOLOv5
In China,fruits have become one of the indispensable foods in people's life,and fruit detection and ripening analysis is an important research direction in the field of agricultural production and food processing.Traditional methods rely on manual operation,which is time-consuming and error-prone.The automated method based on deep learning can improve the accuracy and reduce the cost,which has the prospect of wide application.Therefore,this paper researches and designs a deep learning-based fruit detection and ripeness analysis system,which is mainly based on PyTorch deep learning framework to build YOLOv5 algorithm to detect and identify ripe and unripe fruits of strawberries,apples,and bananas,which can greatly improve the detection efficiency and accuracy,and it has a certain practical significance and practical value.
fruit detectionripeness analysisYOLOv5deep learning
万方数据
维普
