Detection of tomato organs based on convolutional neural network under the overlap and occlusion backgrounds

Traditional detection methods are not sensitive to small-sized tomato organs (flowers and fruits), because the immature green tomatoes are highly similar to the background color. The overlap among fruits and the occlusion of stems and leaves on tomato organs can lead to false and missing detection, which decreases the accuracy and generalization ability of the model. Therefore, a tomato organ recognition method based on improved Feature Pyramid Network was proposed in this paper. To begin with, multi-scale feature fusion was used to fuse the detailed bottom features and high-level semantic features to detect small-sized tomato organs to improve recognition rate. And then repulsion loss was used to take place of the original smooth L 1 loss function. Besides, Soft-NMS (Soft non-maximum suppression) was adopted to replace non-maximum suppression to screen the bounding boxes of tomato organs to construct a recognition model of tomato key organ. Finally, the network was trained and verified on the collected image data set. The results showed that compared with the traditional Faster R-CNN model, the performance was greatly improved (mean average precision was improved from 90.7 to 99.5%). Subsequently, the training model can be compressed so that it can be embedded into the microcontroller to develop further precise pesticide targeting application system of tomato organs and the automatic picking device.