Lanzhou lily is the only kind of sweet lily in China. However, its yield and quality have decreased significantly in recent years due to gray mold disease, bulb rot disease and other diseases. In order to improve the anti-interference ability of Lanzhou lily diseases diagnosis model, only 4, 8, 16, 32, 32 feature maps were selected from the VGG16 model's five pooling layers removing 75% of all feature maps. In the process of fusion, it was found that the average pooling was better than the maximum pooling and the maximum feature selection was superior to random feature selection in the analysis of the diagnosis accuracy and anti-noise performance. The result shown that the accuracy of Lanzhou lily diseases diagnosis reached 97.82% by using the multi-scale fusion CNN with the average pooling and maximum feature selection. In addition, the anti-interference ability of Multi-scale fusion CNN was obviously better than that of VGG 16 model for Gaussian noise, salt-and-pepper noise and speckle noise. The diagnosis CNN constructed in this paper can provide technical support for digitized field management of Lanzhou lily.
In order to improve the classification accuracy of weeds associated with lily in real field condition, this paper constructs a dual attention module from both channel attention and spatial attention, and the dual attention module is embedded into the simplified Inception network. It not only reduces the scale of the model but also improves the detection accuracy. The experimental results show that the training parameters of the model decreased by nearly 90%, and the classification accuracy increased from 94.26% to 98.32%. Furthermore, before embedding, 3.10%, 1.32%, 9.79% and 4.15% of S. wightianus, S. vulgaris, S. viridis and E. pilosa Were mistakenly detected as lily leaves and after embedding, only 1.03% of S. wightianus and 1.81% of E. pilosa were mistakenly detected as lily leaves. This study can provide reference for the next step of pesticide variable spraying research.
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