Abstract
The problem of plant diseases is an important issue affecting the growth of world food. However, the existing technology still has many defects in the automatic detection of plant diseases. These defects are mainly concentrated in three aspects: lack of dataset, no algorithm suitable for large range detection and lack of systems used on agricultural production. In this paper, we have made the following contributions to these shortcomings. First, we proposed a multi-stage system which could not only do the plant species classification but also do the disease classification at the same time. Besides, this approach could also reduce the dependence of the model on dataset to some extent. Second, an improved network proposed by us could perform fast calculations while maintaining accuracy. Third, we have realized the function of real-time leaf disease recognition on the embedded platform, which would provide ideas for the plant diseases detection of a wide range.
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This work was supported by the National Natural Science Foundation of China under Grant 62073129.
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Deng, S., Cheng, L., Li, W., Sun, W., Wang, Y., Liang, Q. (2021). Automatic Leaf Diseases Detection System Based on Multi-stage Recognition. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12888. Springer, Cham. https://doi.org/10.1007/978-3-030-87355-4_21
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