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RESEARCH ARTICLE
Contents Vol 76(3)

Detection and severity assessment of tea leaf blight from UAV remote sensing images

Yongcheng Jiang A , Binyu Wang A and Gensheng Hu https://orcid.org/0000-0002-0181-0748 B *
+ Author Affiliations
- Author Affiliations

A School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China.

B National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China.

* Correspondence to: hugs2906@sina.com

Handling Editor: Davide Cammarano

Crop & Pasture Science 76, CP23351 https://doi.org/10.1071/CP23351
Submitted: 5 January 2024  Accepted: 20 February 2025  Published: 12 March 2025

© 2025 The Author(s) (or their employer(s)). Published by CSIRO Publishing

Abstract

Context

Tea leaf blight (TLB) stands as one of the most destructive diseases affecting tea plants, posing a significant threat to both the yield and quality of tea crops.

Aims

Our aim is to employ efficient deep learning techniques to achieve precise remote sensing monitoring of TLB in natural environments.

Methods

We present an innovative methodology that leverages the combined power of ECDet and MobileNetv3 for the detection and severity assessment of TLB from unmanned aerial vehicle (UAV) remote sensing images. ECDet is constructed with a lightweight backbone to reduce the complexity of the model, and a MicroEA-FPN feature pyramid structure and a decoupled spatial attention-weighted head to achieve balance between focusing on the detailed information of tea leaves and extracting semantic information from small targets. In addition, transfer learning has been implemented to address the performance degradation owing to low UAV image resolution, and the MobileNetv3 is used to improve the accuracy of severity assessment.

Key results

The accuracy of our method was 78.46% in detecting TLB and 83.57% in assessing the severity levels of TLB leaves.

Conclusions

Compared with other object detection and assessing methods, this proposed method achieved a good balance by maintaining high accuracy while requiring fewer parameters and computational resources.

Implications

The proposed method will aid farmers, policymakers, and researchers in better understanding the impact of the TLB disease on tea yield and in taking timely and effective measures.

Keywords: crop protection, deep learning, EfficientNet, severity assessment, target detection, tea leaf blight, transfer learning, UAV.

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