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Lightweight Plant Disease Classification Combining GrabCut Algorithm, New Coordinate Attention, and Channel Pruning

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Abstract

In this paper, a lightweight convolutional neural network is proposed for the classification of plant diseases, containing 63 classes of states for 11 plant species. The different context of experimental data and data in the real environment, insufficient accuracy of the model classification, and oversized model are three main problems of deep learning techniques applied to agricultural production. In this paper, we mainly focus on these three problems. First, the GrabCut algorithm is adopted to unify the background of the experimental data and the real data to black, allowing the trained model to have the same good effect when applied in practice. Then, we propose a new coordinate attention block to improve the classification accuracy of convolutional neural networks and empirically demonstrate the effectiveness of our approach with several state-of-the-art CNN models. Finally, channel pruning is applied to the trained model, which reduces the model size and computational effort by 85.19\(\%\) and 92.15\(\%\) respectively with little change in the model accuracy, making it better suited for agricultural platforms with lower memory and computational capacity.

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Acknowledgements

This work is supported by China Agriculture Research System of MOF and MARA, the Project of Scientific and Technological Innovation Planning of Hunan Province (2020NK2008), Hunan Province Modern Agriculture Technology System for Tea Industry, the National Natural Science Foundation of China (42130716). We are grateful to the High Performance Computing Center of Central South University for partial support of this work.

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  1. School of Computer Science, Central South University, Lushannan, Changsha, 410083, Hunan, China

    Fang Qi, Yongle Wang & Zhe Tang

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  1. Fang Qi
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  2. Yongle Wang
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  3. Zhe Tang
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Correspondence to Zhe Tang.

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Qi, F., Wang, Y. & Tang, Z. Lightweight Plant Disease Classification Combining GrabCut Algorithm, New Coordinate Attention, and Channel Pruning. Neural Process Lett 54, 5317–5331 (2022). https://doi.org/10.1007/s11063-022-10863-0

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