Corn Diseases Recognition Method Based on Multi-feature Fusion and Improved Deep Belief Network

Abstract

The automatic monitoring timely and accurately of crop diseases has become an important research field in the process of intelligent management of precision agriculture. Aiming at the low application rate of current crop disease identification methods in real field environment, a disease identification method based on multi-feature fusion and improved deep belief network is proposed. We Obtained representative samples in the field, and augmented the data set. K-means clustering segmentation and morphological corrosion processing were utilized to obtain segmentation maps with clear boundaries and low noise. Then color features, shape features and textures of disease images were extracted and they were fused to normalize as input data. A corn disease recognition model based on deep belief network was designed, using labeled and unlabeled dual hidden layer network structure to study the DBN hidden layer node combination. We obtained the optimal hidden layer node number combination method for disease classification: [26,85,29,4], and the accuracy of DBN was 92.79%. On this basis, the deep belief network recognition model was optimized by particle swarm optimization algorithm. The experiment indicated that recognition effect using multi-feature fusion as input vectors was better than a single feature. The improved PSO-DBN had a faster convergence speed than the standard DBN, it has a recognition accuracy of 3.86% higher than that of DBN. Compared with state-of-the-art methods including SVM, ANN and CNN models, the proposed deep belief network can effectively dig deep into the digital features of disease areas or lesions and has the best performance, which could meet the needs of intelligent identification of field diseases.