Deep migration learning-based recognition of diseases and insect pests in Yunnan tea under complex environments.

ABSTRACT:

Background

The occurrence, development, and outbreak of tea diseases and pests pose a significant challenge to the quality and yield of tea, necessitating prompt identification and control measures. Given the vast array of tea diseases and pests, coupled with the intricacies of the tea planting environment, accurate and rapid diagnosis remains elusive. In addressing this issue, the present study investigates the utilization of transfer learning convolution neural networks for the identification of tea diseases and pests. Our objective is to facilitate the accurate and expeditious detection of diseases and pests affecting the Yunnan Big leaf kind of tea within its complex ecological niche.

Results

Initially, we gathered 1878 image data encompassing 10 prevalent types of tea d

SUBMITTER: Li Z 

PROVIDER: S-EPMC11229499 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature