Fig. 9

Flowchart of the deep convolutional network based approach for cotton seedling detection and counting. After preprocessing, video frames are fed into a trained Faster RCNN model for seedling detection. Detected seedlings are associated using a Kalman filter-based video tracking algorithm, so the same plant object will be tracked through video frames and not counted repeatedly