关键词: 多元宇宙, 卷积神经网络, 番茄病害, 图像识别

Abstract: In order to improve the effect of convolutional neural network on tomato disease image recognition, an improved multiverse optimizer algorithm was proposed. Firstly, the ports of wormholes were designed, and the ratio range between the number of ports of the white hole and the black hole was set to enable movement of the verse in multiple directions through wormholes. Secondly, an information transfer model of the verse was established based on the twoway motion, allowing for faster evolution and enabling the verse to obtain information from the optimal verse. Additionally, the expansion rate of the verse was corrected using nonlinear adjustment. Thirdly, the texture, color, and shape features of tomato diseases were extracted, and the activation and loss functions of the convolutional neural network were designed using the Spearman correlation coefficient to determine the optimal number of parameters in the multiverse. Finally, the algorithm flow was given. The experimental simulation showed that the proposed algorithm had a higher recognition accuracy compared to other algorithms, with an average value of 97.34% for bacterial spot, 97.03% for early blight, 9708% for late blight, 97.14% for leaf mold, 97.12% for leaf spot, and 97.20% for spider mite, while also having a lower consumption time than other algorithms.

Key words: multiverse, convolutional neural network, tomato disease, image recognition