Detection of Multi-varieties of On-tree Mangoes using MangoYOLO5

Automated harvesting and detection of fruits are crucial for agronomic applications like estimation and mapping of yield. Earlier, fruit detection methods were mostly dependent on hand-crafted features and were prone to changes in the actual orchard environment. However, recently deep learning-based methods especially one-stage object detection techniques like YOLO has achieved a higher detection accuracy to detect different fruits including mango in on-tree orchard images. In our previous work, we proposed a lightweight YOLOv5 model named "MangoYOLO5" for the detection of mangoes, and we have achieved an accuracy of 94.4% on one variety. Now, we have created a dataset of seven varieties of on-tree mangoes, with four varieties being publicly available, and the other three varieties from a local mango orchard using a UAV. We have tried detecting these seven varieties using the MangoYOLO5 model and achieved an average accuracy of 92%. It shows that the mango detection performance is 3.4% better than the YOLOv5s, taking into several characteristics like occlusion, distance, and lighting conditions. Additionally, compared to the original YOLOv5s, the achieved lighter model requires 55.55% less training time, which can significantly affect on real-time implementations.