  In this research, an attention-based depthwise separable neural network with
Bayesian optimization (ADSNN-BO) is proposed to detect and classify rice
disease from rice leaf images. Rice diseases frequently result in 20 to 40 \%
corp production loss in yield and is highly related to the global economy.
Rapid disease identification is critical to plan treatment promptly and reduce
the corp losses. Rice disease diagnosis is still mainly performed manually. To
achieve AI assisted rapid and accurate disease detection, we proposed the
ADSNN-BO model based on MobileNet structure and augmented attention mechanism.
Moreover, Bayesian optimization method is applied to tune hyper-parameters of
the model. Cross-validated classification experiments are conducted based on a
public rice disease dataset with four categories in total. The experimental
results demonstrate that our mobile compatible ADSNN-BO model achieves a test
accuracy of 94.65\%, which outperforms all of the state-of-the-art models
tested. To check the interpretability of our proposed model, feature analysis
including activation map and filters visualization approach are also conducted.
Results show that our proposed attention-based mechanism can more effectively
guide the ADSNN-BO model to learn informative features. The outcome of this
research will promote the implementation of artificial intelligence for fast
plant disease diagnosis and control in the agricultural field.
