论文标题
使用基于注意的神经网络和贝叶斯优化的水稻疾病检测和分类
Rice Diseases Detection and Classification Using Attention Based Neural Network and Bayesian Optimization
论文作者
论文摘要
在这项研究中，提出了具有贝叶斯优化（ADSNN-BO）的基于注意力的深度可分离神经网络，以检测和分类水稻图像中的水稻疾病。水稻疾病经常导致20至40 \％Corp的产量产量损失，并且与全球经济高度相关。快速疾病鉴定对于迅速计划治疗并减少公司损失至关重要。水稻疾病诊断仍主要手动进行。为了实现AI辅助快速，准确的疾病检测，我们提出了基于Mobilenet结构和增强注意机制的ADSNN-BO模型。此外，将贝叶斯优化方法应用于模型的调节超参数。交叉验证的分类实验是基于公共水稻疾病数据集进行的，总共四个类别。实验结果表明，我们的移动兼容ADSNN-BO模型达到了94.65 \％的测试精度，这表现优于测试的所有最新模型。为了检查我们提出的模型的解释性，还进行了功能分析，包括激活图和过滤器可视化方法。结果表明，我们提出的基于注意力的机制可以更有效地指导ADSNN-BO模型来学习信息功能。这项研究的结果将促进人工智能在农业领域的快速植物诊断和控制。
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.
