Fruit detection forms a vital part of the robotic harvesting platform. However, uneven environment conditions, such as branch and leaf occlusion, illumination variation, clusters of tomatoes, shading, and so on, have made fruit detection very challenging. In order to solve these problems, a modified YOLOv3 model called YOLO-Tomato models were adopted to detect tomatoes in complex environmental conditions.
With the application of "label what you see approach", densely architecture incorporation, spatial pyramid pooling and Mish function activation to the modified YOLOv3 model, the YOLO-Tomato models: YOLO-Tomato-A at AP 98.3% with detection time 48 ms, YOLO-Tomato-B at AP 99.3% with detection time 44 ms, and YOLO-Tomato-C at AP 99.5% with detection time 52 ms, performed better than other state-of-the-art methods.
This research work proposed the use of YOLO-Tomato models for tomato detection, based on modified YOLOv3 model. The use of small tomato datasets obtained from complex environment condition to limit deep learning drawbacks, label what you see (LWYS) approach, densely architecture incorporated into YOLOv3 to facilitate reuse of features for well generalize tomato detection, Mish activation and spatial pyramid pooling (SPP) to reduce missed detections and inaccuracies are all adopted to make the detector as intelligent as humans.
The experimental results show that the proposed methods performed better than other state-of-the-art methods with reference to average precision (AP) in particular. The level of YOLO-Tomato models’ performance increases as YOLO-Tomato-C > YOLO-Tomato-B > YOLO-Tomato-A with reference to average precision (AP), while the detection speed of YOLO-Tomato-B > YOLO-Tomato-A > YOLO-Tomato-C. In all, the YOLO-Tomato models show better generalization and real-time tomatoes’ detection, which is applicable for harvesting robots.
Lawal, Olarewaju. (2021). Tomato detection based on modified YOLOv3 framework. Scientific Reports. 11. 10.1038/s41598-021-81216-5.