Lu Yang, Du Jiaojiao, Liu Pengfei, Zhang Yong, Hao Zhiqiang
College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing, China.
School of Physics and Electronic Engineering, Northeast Petroleum University, Daqing, China.
Front Bioeng Biotechnol. 2022 Apr 27;10:855667. doi: 10.3389/fbioe.2022.855667. eCollection 2022.
Rice blast, rice sheath blight, and rice brown spot have become the most popular diseases in the cold areas of northern China. In order to further improve the accuracy and efficiency of rice disease diagnosis, a framework for automatic classification and recognition of rice diseases is proposed in this study. First, we constructed a training and testing data set including 1,500 images of rice blast, 1,500 images of rice sheath blight, and 1,500 images of rice brown spot, and 1,100 healthy images were collected from the rice experimental field. Second, the deep belief network (DBN) model is designed to include 15 hidden restricted Boltzmann machine layers and a support vector machine (SVM) optimized with switching particle swarm (SPSO). It is noted that the developed DBN and SPSO-SVM can simultaneously learn three proposed features including color, texture, and shape to recognize the disease type from the region of interest obtained by preprocessing the disease images. The proposed model leads to a hit rate of 91.37%, accuracy of 94.03%, and a false measurement rate of 8.63%, with the 10-fold cross-validation strategy. The value of the area under the receiver operating characteristic curve (AUC) is 0.97, whose accuracy is much higher than that of the conventional machine learning model. The simulation results show that the DBN and SPSO-SVM models can effectively extract the image features of rice diseases during recognition, and have good anti-interference and robustness.
稻瘟病、水稻纹枯病和水稻褐斑病已成为中国北方寒冷地区最常见的病害。为了进一步提高水稻病害诊断的准确性和效率，本研究提出了一种水稻病害自动分类与识别框架。首先，我们构建了一个训练和测试数据集，包括1500张稻瘟病图像、1500张水稻纹枯病图像和1500张水稻褐斑病图像，并从水稻试验田中采集了1100张健康图像。其次，设计了深度信念网络（DBN）模型，包括15个隐藏的受限玻尔兹曼机层和一个用切换粒子群（SPSO）优化的支持向量机（SVM）。需要注意的是，所开发的DBN和SPSO-SVM可以同时学习包括颜色、纹理和形状在内的三个特征，以从对病害图像进行预处理得到的感兴趣区域中识别病害类型。采用10折交叉验证策略，所提出的模型命中率为91.37%，准确率为94.03%，误测率为8.63%。接收器操作特征曲线（AUC）下的面积值为0.97，其准确率远高于传统机器学习模型。仿真结果表明，DBN和SPSO-SVM模型在识别过程中能够有效提取水稻病害的图像特征，具有良好的抗干扰能力和鲁棒性。
