面向农作物病害识别的高阶残差卷积神经网络研究

Research on high-order residual convolution neural network for crop disease recognition application

当前研究农作物病害的准确识别工作中,针对简单背景的农作物病害图像识别取得了巨大成功,但当面向包含有各种噪声和复杂背景真实场景的农作物病害图像识别问题时,难以满足识别准确率的要求.为此提出了一种新的面向农作物病害识别应用的高阶残差卷积神经网络方法,以实现农作物病害的准确、抗干扰的识别.实验结果表明,该方法具有高准确率、强鲁棒性和良好的抗干扰能力,能较好地满足农作物病害识别的实际应用需求.

Current research works focusing on the image recognition of crop disease in simple background have achieved great success.However,when handling the problem of crop disease recognition with various noise and complex backgrounds,it is difficult to meet the requirement of recognition accuracy.To address these issues,a new high-order residual convolution neural network for crop disease recognition is proposed,which can realize crop disease recognition that is both accurate and anti-interference.Extensive experimental results demonstrate that the proposed method has high accuracy,strong robustness as well as good anti-interference ability,and can better meet the practical application requirements for crop disease recognition.