Open Access

ARTICLE

Differentiation of Wheat Diseases and Pests Based on Hyperspectral Imaging Technology with a Few Specific Bands

Lin Yuan¹, Jingcheng Zhang^2,*, Quan Deng², Yingying Dong³, Haolin Wang², Xiankun Du²
1 School of Information Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou, 310018, China
2 School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China
3 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China
* Corresponding Author: Jingcheng Zhang. Email:
(This article belongs to this Special Issue: Symbiotic Associations for Nutrients Management and Complexes Formation for Better Agricultural Crops Productivity under Biotic and Abiotic Stresses)

Phyton-International Journal of Experimental Botany 2023, 92(2), 611-628. https://doi.org/10.32604/phyton.2022.023662

Received 09 May 2022; Accepted 11 July 2022; Issue published 12 October 2022

Abstract

Hyperspectral imaging technique is known as a promising non-destructive way for detecting plants diseases and pests. In most previous studies, the utilization of the whole spectrum or a large number of bands as well as the complexity of model structure severely hampers the application of the technique in practice. If a detection system can be established with a few bands and a relatively simple logic, it would be of great significance for application. This study established a method for identifying and discriminating three commonly occurring diseases and pests of wheat, i.e., powdery mildew, yellow rust and aphid with a few specific bands. Through a comprehensive spectral analysis, only three bands at 570, 680 and 750 nm were selected. A novel vegetation index namely Ratio Triangular Vegetation Index (RTVI) was developed for detecting anomalous areas on leaves. Then, the Support Vector Machine (SVM) method was applied to construct the discrimination model based on the spectral ratio analysis. The validating results suggested that the proposed method with only three spectral bands achieved a promising accuracy with the Overall Accuracy (OA) of 83%. With three bands from the hyperspectral imaging data, the three wheat diseases and pests were successfully detected and discriminated. A stepwise strategy including background removal, damage lesions recognition and stresses discrimination was proposed. The present work can provide a basis for the design of low cost and smart instruments for disease and pest detection.

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Keywords

Winter wheat; diseases; pests; hyperspectral imaging; discriminant analysis

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