In this paper, a method for detecting rapid rice disease based on FCM-KM and Faster R-CNN fusion is proposed to address various problems with the rice disease images, such as noise, blurred image edge, large background interference and low detection accuracy. Firstly, the method uses a twodimensional filtering mask combined with a weighted multilevel median filter (2DFM-AMMF) for noise reduction, and uses a faster two-dimensional Otsu threshold segmentation algorithm (Faster 2D-Otsu) to reduce the interference of complex background with the detection of target blade in the image. Then the dynamic population firefly algorithm based on the chaos theory as well as the maximum and minimum distance algorithm is applied for optimization of the K-Means clustering algorithm (FCM-KM) to determine the optimal clustering class k value while addressing the tendency of the algorithm to fall into the local optimum problem. Combined with the R-CNN algorithm for the identification of rice diseases, FCM-KM analysis is conducted to determine the different sizes of the Faster R-CNN target frame. As revealed by the application results of 3010 images, the accuracy and time required for detection of rice blast, bacterial blight and blight were 96.71%/0.65s, 97.53%/0.82s and 98.26%/0.53s, respectively, indicating clearly that the method is more capable of detecting rice diseases and improving the identification accuracy of Faster R-CNN algorithm, while reducing the time required for identification.

The identification of maize leaf diseases will meet great challenges because of the difficulties in extracting lesion features from the constant-changing environment, uneven illumination reflection of the incident light source and many other factors. In this paper, a novel maize leaf disease recognition method is proposed. In this method, we first designed a maize leaf feature enhancement framework with the capability of enhancing the features of maize under the complex environment. Then a novel neural network is designed based on backbone Alexnet architecture, named DMS-Robust Alexnet. In the DMS-Robust Alexnet, dilated convolution and multi-scale convolution are combined to improve the capability of feature extraction. Batch normalization is performed to prevent network over-fitting while enhancing the robustness of the model. PRelu activation function and Adabound optimizer are employed to improve both convergence and accuracy. In experiments, it is validated from different perspectives that the maize leaf disease feature enhancement algorithm is conducive to improving the capability of the DMS-Robust Alexnet identification. Our method demonstrates strong robustness for maize disease images collected in the natural environment, providing a reference for the intelligent diagnosis of other plant leaf diseases. INDEX TERMS Image enhancement, dilated convolution, multi-scale convolution, maize leaf disease, convolutional neural network.

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In existing machine vision technology for fruit defects, the hue appears different, and the defect area is small due to the irregularity of illumination reflection from the surface incident light source, this makes it difficult to extract the defect area. Thus, we proposed an apple defect detection method based on the Fuzzy C-means Algorithm and the Nonlinear Programming Genetic Algorithm (FCM-NPGA) in combination with a multivariate image analysis. First, the image was denoised and enhanced through fractional differentiation. The noise points and edge points were removed, and the important texture information was preserved. Then, the FCM-NPGA algorithm was used to segment the suspicious defect graph. Finally, a method based on a multivariate image analysis strategy was used to detect the flaws of the apple's suspicious defect map. The application results of 2000 images showed that the overall detection accuracy was 98%. Experiments show that the apple defect detection algorithm based on FCM and NPGA combined with multi-image analysis method is effective. INDEX TERMS Apple defect detection, fractional calculus, multivariate image analysis, FCM, NPGA.

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There are two major questions regarding Environmental Sound Classification (ESC). What is the best audio recognition framework, and what is the most robust audio feature? For investigating above problems, the Gated Recurrent Unit (GRU) network was used to analyze the effect of single features such as Mel Scale Spectrogram (Mel), Log-Mel Scale Spectrogram (LM), and Mel frequency cepstral coefficient (MFCC) as well as multi-feature about Mel-MFCC, LM-MFCC, and Mel-LM-MFCC (T-M) in this paper. The experiment results show that in the ESC tasks, multi-features are better than the single features in the same dimensions, and LM-MFCC has the strongest robustness. Meanwhile, reverse sequence MFCC (R-MFCC) and forward and reverse mixed sequence MFCC (FR-MFCC) were also proposed to study the effects of sequence changes on audio. The experiment results show that the sequence transformation of audio features has less influence on the recognition tasks. Furthermore, to investigate the ESC task further we introduced the attention weight similar model (AWS) in to the multi-feature. The AWS model allows different audio feature attention weights of the same sound to learn from each other. It makes the GRU-AWS model focus on the frame-level features more effectively. The experiment results show that the GRU-AWS gets excellent performance with a recognition rate of 94.3%, and it outperforms other state-of-the-art methods.

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