计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 363-369.doi: 10.11896/jsjkx.210500044
高荣华1,2, 白强1,2,3, 王荣1,2,3, 吴华瑞1,2, 孙想1,2
GAO Rong-hua1,2, BAI Qiang1,2,3, WANG Rong1,2,3, WU Hua-rui1,2, SUN Xiang1,2
摘要： 在作物染病早期,及时获取作物病害信息,判别染病原因和严重程度,从而对症下药,能够及时防治病害扩散造成的作物产量下降。针对传统深度学习网络对作物早期病害识别方法准确率低的问题,基于病害特征图像各通道包含的信息量不同,及多层感知机(Multilayer Perceptron,MLP)能逼近任意函数的特点,提出了一种改进注意力机制的多叉树网络作物早期病害识别方法,将注意力机制融合残差网络对病害特征重校准(SMLP_Res);同时结合多叉树结构对具有较高特征提取能力的SMLP_ResNet(Squeeze-Multi-Layer Perceptron ResNet)网络进行扩展,构建的多叉树SMLP_ResNet网络模型可以简化多作物早期病害识别任务,有效提取早期病害特征。实验中使用Plant Village和AI Challenger 2018两种数据集对18层的ResNet,SE_ResNet,SMLP_ResNet这3种网络模型,以及同等结构的多叉树结构模型进行训练,验证了SMLP_Res和多叉树结构对作物病害识别模型的影响。通过实验分析得到18层的ResNet,SE_ResNet,SMLP_ResNet这3种网络模型在病害特征较明显的Plant Village数据集上病害识别的准确率均达到99%以上,但在早期病害数据集AI Challenger 2018上的准确率均不超过87%,SMLP_ResNet因加入了SMLP_Res模块,故对作物病害数据特征提取较为充分,检测结果较好。多叉树结构的3种早期病害识别模型,在AI Challenger 2018数据集上识别准确率均有明显提升,多叉树SMLP_ResNe较其余两种模型具有较好的性能,其中樱桃早期病害识别准确率为99.13%,检测结果最佳。文中提出的多叉树SMLP_ResNet作物早期病害识别模型,能够简化识别任务,抑制噪声传输,达到了较高的准确率。
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