Plant fruit counting using context aware feature and point to point pairing

Plant fruit count of can predict the yield of the whole orchard, which has the key guiding effect to agricultural production process. In this work, we propose a context feature aggregated convolutional neural network for plant fruit counting. The feature extracted by VGG network is pooled to get the context information, which is concatenated with the original feature for classification and regression. The output of the network is point, which is matched to the ground truth point to define the loss. Experimental results show that compared with existing network structure for fruit counting, our method achieves better counting performance on public available fruit dataset. The improvement of accuracy indicators shows that the proposed method has a good effect on plant images with different scales, illumination, contrast and occlusion.