基于参数指数非线性残差神经网络的脐橙病变叶片识别

提出一种参数指数非线性(PENL)函数来改进残差网络,利用深度学习的新方法识别脐橙叶面病变,减少了整流线性损失,提升了训练效果。以脐橙叶面图像为样本,进行CNN训练,以区分出病变、缺素、正常及非此类物种4种类型,实现了对于脐橙疾病检测迅速且方便应用的分类模型,相比于传统植物病变识别方法具有极大的优势,最终识别准确率达到了97.18%~98.86%。

Identification of navel orange lesions leaves based on parametric exponential non-linear residual neural network

In order to better identify navel orange leaf lesions. The paper proposed a parametric exponential nonlinear function asactivation function to improve the residual network and provided a new method of deep learning to identify lesions of navel orange leaves, which reduced the linear loss of rectification and improves the training effect. In this paper, CNN was used as a training tool, and navel orange leaf images were selected as training samples to discriminate the four types of diseased, deficient, normal and non-species. This new identification method had the great advantage of being convenient to use and more accurate for the identification of traditional plant diseases, and finally reached the accuracy rate of 97.18%-98.86%.