Unmanned aerial vehicle hierarchical detection of leaf blast in rice crops based on a specific spectral vegetation index

Highlights

● A new vegetation index, rice blast index (RBI), was constructed to detect rice leaf blast.

● The disease detection performance of RBI, TVI, DDI and MTVI1 vegetation indices were compared.

● The level of leaf blast disease in the field was evaluated using the new RBI.

Abstract

Leaf blast is a significant global problem, severely affecting rice quality and yield, making swift, non-invasive detection crucial for effective field management. This study used hyperspectral remote sensing technology via an unmanned aerial vehicle to gather spectral data from rice crops. ANOVA and the Relief-F algorithm were used to identify spectral bands sensitive to the disease and developed a new vegetation index, the rice blast index (RBI). This RBI was compared with 30 established vegetation indexes, using correlation analysis and visual comparison to further shortlist six superior indexes, including RBI. These were evaluated using the K-nearest neighbor (KNN) and random forests (RF) classification models. RBI demonstrated superior detection accuracy for leaf blast in both the KNN model (95.0% overall accuracy and 93.8% kappa coefficient) and the RF model (95.1% overall accuracy and 92.5% kappa coefficient). This study highlights the significant potential of RBI as an effective tool for precise leaf blast detection, offering a powerful new mechanism and theoretical basis for enhanced disease management in rice cultivation.

Graphical abstract

Keywords

Drone remote sensing technology / hyperspectral technology / leaf blast disease / rice / vegetation index

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Compliance with ethics guidelines

Guangming Li, Dongxue Zhao, Jinpeng Li, Shuai Feng, and Chunling Chen declare that they have no conflicts of interest or financial conflicts to disclose. This article does not contain any studies with human or animal subjects performed by any of the authors.

RIGHTS & PERMISSIONS

The Author(s) 2024. Published by Higher Education Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0)