Effects of Image Dataset Configuration on the Accuracy of Rice Disease Recognition Based on Convolution Neural Network.

ABSTRACT: In recent years, the convolution neural network has been the most widely used deep learning algorithm in the field of plant disease diagnosis and has performed well in classification. However, in practice, there are still some specific issues that have not been paid adequate attention to. For instance, the same pathogen may cause similar or different symptoms when infecting plant leaves, while the same pathogen may cause similar or disparate symptoms on different parts of the plant. Therefore, questions come up naturally: should the images showing different symptoms of the same disease be in one class or two separate classes in the image database? Also, how will the different classification methods affect the results of image recognition? In this study, taking rice leaf blast and neck blas

SUBMITTER: Zhou H 

PROVIDER: S-EPMC9295741 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature