Measurement of tomato fruits quantity at different ripening stages based on color point cloud images

Objective  In order to measure the number of tomato fruits at different ripening stages in greenhouse, a method based on color point cloud images was proposed.

Method  The image information of tomato in greenhouse was collected by KinectV2.0 on the mobile platform to synthesize the tomato plant point cloud, then the tomato plant point clouds from two perspectives were synthesized into a point cloud, and the point cloud of nearby tomato plant was obtained by depth information interception. The labeled point cloud data were input into the PointRCNN object detection network to train the prediction model and recognize tomato fruit in the tomato plant point cloud. Finally, support vector machine(SVM) classifier based on feature matrix training was used to classify ripeness of the identified fruits, and the number of tomato fruits at different ripening stages was obtained.

Result  The precision rate of the method based on PointRCNN object detection network for identifying the number of tomato fruits was 86.19% and the recall rate was 83.39%. The accuracy of SVM classifier based on feature matrix training for predicting the ripeness of tomato fruits was 94.27% in the training set and 96.09% in the test set.

Conclusion  The measurement method based on color point cloud images can accurately identify the number of tomato fruits at different ripening stages, and provide data supports for evaluating the yield of tomato fruits in greenhouse.