UAV-Based Multimodal Monitoring of Tea Anthracnose with Temporal Standardization

Qimeng Yu, Jingcheng Zhang, Lin Yuan (), Xin Li, Fanguo Zeng, Ke Xu, Wenjiang Huang and Zhongting Shen
Additional contact information
Qimeng Yu: College of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, China
Jingcheng Zhang: College of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, China
Lin Yuan: School of Computer Science and Technology, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China
Xin Li: State Key Laboratory of Tea Plant Germplasm Innovation and Resource Utilization, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
Fanguo Zeng: School of Computer Science and Technology, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China
Ke Xu: School of Computer Science and Technology, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China
Wenjiang Huang: State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Zhongting Shen: School of Computer Science and Technology, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China

Agriculture, 2025, vol. 15, issue 21, 1-25

Abstract: Tea Anthracnose (TA), caused by fungi of the genus Colletotrichum , is one of the major threats to global tea production. UAV remote sensing has been explored for non-destructive and high-efficiency monitoring of diseases in tea plantations. However, variations in illumination, background, and meteorological factors undermine the stability of cross-temporal data. Data processing and modeling complexity further limits model generalizability and practical application. This study introduced a cross-temporal, generalizable disease monitoring approach based on UAV multimodal data coupled with relative-difference standardization. In an experimental tea garden, we collected multispectral, thermal infrared, and RGB images and extracted four classes of features: spectral (Sp), thermal (Th), texture (Te), and color (Co). The Normalized Difference Vegetation Index (NDVI) was used to identify reference areas and standardize features, which significantly reduced the relative differences in cross-temporal features. Additionally, we developed a vegetation–soil relative temperature (VSRT) index, which exhibits higher temporal-phase consistency than the conventional normalized relative canopy temperature (NRCT). A multimodal optimal feature set was constructed through sensitivity analysis based on the four feature categories. For different modality combinations (single and fused), three machine learning algorithms, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Multi-layer Perceptron (MLP), were selected to evaluate disease classification performance due to their low computational burden and ease of deployment. Results indicate that the “Sp + Th” combination achieved the highest accuracy (95.51%), with KNN (95.51%) outperforming SVM (94.23%) and MLP (92.95%). Moreover, under the optimal feature combination and KNN algorithm, the model achieved high generalizability (86.41%) on independent temporal data. This study demonstrates that fusing spectral and thermal features with temporal standardization, combined with the simple and effective KNN algorithm, achieves accurate and robust tea anthracnose monitoring, providing a practical solution for efficient and generalizable disease management in tea plantations.

Keywords: tea anthracnose; UAV; remote sensing; monitoring; data fusion (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/21/2270/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/21/2270/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:15:y:2025:i:21:p:2270-:d:1784023

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().