MS-Net: a novel lightweight and precise model for plant disease identification.

ABSTRACT: The rapid development of image processing technology and the improvement of computing power in recent years have made deep learning one of the main methods for plant disease identification. Currently, many neural network models have shown better performance in plant disease identification. Typically, the performance improvement of the model needs to be achieved by increasing the depth of the network. However, this also increases the computational complexity, memory requirements, and training time, which will be detrimental to the deployment of the model on mobile devices. To address this problem, a novel lightweight convolutional neural network has been proposed for plant disease detection. Skip connections are introduced into the conventional MobileNetV3 network to enrich the input featur

SUBMITTER: Quan S 

PROVIDER: S-EPMC10641454 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature