Abstract
Tea trees are often vulnerable to diseases and insect pests in the process of growth, resulting in the decline of tea production and quality. It is of great significance to identify and prevent tea leaf diseases in time to ensure the steady increase of tea. This study proposes a tea leaf disease identification method based on deep transfer learning, which improves the recognition accuracy of the model through knowledge transfer. Besides, for the unbalanced distribution of the number of samples, the cross-entropy loss function is replaced with the focal loss function, which further improves the identification effect of the model. The experiment shows that the identification model of tea leaf disease proposed in this study can achieve the accuracy of more than 90.42%, which verifies the effectiveness of this research and has important theoretical and practical significance in promoting the development of intelligent agriculture.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
