Temporal Analysis of Tea Shoot Growth Based on Canopy Imaging and Deep Learning
Journal
2024 ASABE Annual International Meeting
Part Of
2024 ASABE Annual International Meeting
ISBN (of the container)
9798331302214
Date Issued
2024
Author(s)
Abstract
Monitoring the growth status of tea trees in a tea garden requires significant time and manpower to determine the temporal trend of tea shoot growth. In this study, we propose an approach to use tea canopy images acquired from field to calculate the Tea Shoot Density Index (TSDI) of the tea tree. The TSDI can be used to estimate the yield of tea leaves in tea gardens and assist in various management operations. By integrating deep learning models of YOLO V8 instance segmentation with DINO V2 depth estimation, tea shoot object recognition accuracy is enhanced, enabling the precise calculation of the TSDI. This leads to developing a tea shoot growth model that provides a quantified pattern for evaluating tea tree growth curve assessment. The proposed method eliminates the necessity of a complex 3D imaging system by leveraging just a standard monocular RGB camera for tea shoot detection. Through the integration of an embedding system designed for continuous image acquisition across the tea canopy, our monitoring framework facilitates comprehensive evaluation and analysis of field tea tree growth. This approach offers insights into both temporal and spatial dimensions, empowering tea garden managers to optimize cultivation practices and elevate the quality of tea products.
Event(s)
2024 American Society of Agricultural and Biological Engineers Annual International Meeting (ASABE 2024), Anaheim, 28 July 2024 through 31 July 2024
Subjects
Deep Learning
Growth Curve Modeling
Tea Shoot Density
Publisher
American Society of Agricultural and Biological Engineers
Type
conference paper