Peach Disease Image Detection Using Asymptotic Non-Local Means and PCNN-IPELM PROJECT TITLE : Detection of Peach Disease Image Based on Asymptotic Non-Local Means and PCNN-IPELM ABSTRACT: This paper proposes a peach disease detection method based on the asymptotic non-local means (ANLM) image algorithm and the fusion of parallel convolution neural network (PCNN) and extreme learning machine (ELM) optimized by linear particle swarm optimization, which addresses the problems of noise, background interference, and low detection in peach disease images (IPSO). To identify the characteristics of peach disease, the method first uses the ANLM image denoising algorithm, then uses the parallel convolution neural network proposed in this paper, uses the improved elu activation function instead of the conventional ReLu activation function, and uses the linear particle swarm optimized ELM (IPEL). The identification accuracy of brown rot, black spot, anthracnose, scab, and normal peach was 89.02, 90.56, 85.37, 86.70, and 89.91 percent, respectively, based on 25513 images, indicating that this method was an effective way for peach disease diagnosis. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Detection of Malicious Social Bots Using Learning Automata With URL Features in Twitter Network Prediction of Diabetes Using a Combination of Machine Learning Classifiers
