Image Classification and Recognition of Rice Diseases: A Hybrid DBN and Particle Swarm Optimization Algorithm - Lu, Yang; Du, Jiaojiao; Liu, Pengfei; Zhang, Yong; Hao, Zhiqiang (Frontiers in Bioengineering and Biotechnology, 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 diseases diagnosis, a framework for automatic classification and recognition of rice diseases is proposed in this paper. Firstly, we construct a training and testing data set including 1500 images of rice blast, 1500 images of rice sheath blight, 1500 images of rice brown spot and 1100 healthy images were collected from the rice experimental field. Secondly, the deep belief network (DBN) model is designed including 15 hidden restricted Boltzmann machines layers and 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, shape to recognize the disease type from the region of interest that obtained by pre-processing the disease images. The proposed model leads to hit rate 91.37%, accuracy 94.03% and false measurement rate 8.63% respectively with the 10-fold cross-validation strategy. The value of area under the receiver operating characteristic curve (AUC) is 0.97 whose accuracy is much higher than that of conventional machine learning model. The simulation results show that DBN and SPSO-SVM models can effectively extract the image features of rice diseases during recognition, and have good anti-interference and robustness.