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

Junning Shao; Kaiqiong Sun; Zhao Yi; Zhihao Chen

doi:10.1117/12.3000698

7 March 2024 Plant fruit counting using context aware feature and point to point pairing

Junning Shao, Kaiqiong Sun, Zhao Yi, Zhihao Chen

Proceedings Volume 13086, MIPPR 2023: Pattern Recognition and Computer Vision; 130860L (2024) https://doi.org/10.1117/12.3000698
Event: Twelfth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2023), 2023, Wuhan, China

Abstract

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.

Citation Download Citation

Junning Shao, Kaiqiong Sun, Zhao Yi, and Zhihao Chen "Plant fruit counting using context aware feature and point to point pairing", Proc. SPIE 13086, MIPPR 2023: Pattern Recognition and Computer Vision, 130860L (7 March 2024); https://doi.org/10.1117/12.3000698

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