Identification of Rice Sheath Blight through Spectral Responses Using Hyperspectral Images.

Fenfang Lin,Xingen Zhou,Dongyan Zhang,Changwei Tan,Sen Guo

doi:10.3390/s20216243

Abstract

Sheath blight (ShB), caused by Rhizoctonia solani AG1-I, is one of the most important diseases in rice worldwide. The symptoms of ShB primarily develop on leaf sheaths and leaf blades. Hyperspectral remote sensing technology has the potential of rapid, efficient and accurate detection and monitoring of the occurrence and development of rice ShB and other crop diseases. This study evaluated the spectral responses of leaf blade fractions with different development stages of ShB symptoms to construct the spectral feature library of rice ShB based on “three-edge” parameters and narrow-band vegetation indices to identify the disease on the leaves. The spectral curves of leaf blade lesions have significant changes in the blue edge, green peak, yellow edge, red valley, red edge and near-infrared regions. The variables of the normalized index between green peak amplitude and red valley amplitude (Rg − Ro)/(Rg + Ro), the normalized index between the yellow edge area and blue edge area (SDy − SDb)/(SDy + SDb), the ratio index of green peak amplitude and red valley amplitude (Rg/Ro) and the nitrogen reflectance index (NRI) had high relevance to the disease. At the leaf scale, the importance weights of all attributes decreased with the effect of non-infected areas in a leaf by the ReliefF algorithm, with Rg/Ro being the indicator having the highest importance weight. Estimation rate of 95.5% was achieved in the decision tree classifier with the parameter of Rg/Ro. In addition, it was found that the variety degree of absorptive valley, reflection peak and reflecting steep slope was different in the blue edge, green and red edge regions, although there were similar spectral curve shapes between leaf sheath lesions and leaf blade lesions. The significant difference characteristic was the ratio index of the red edge area and green peak area (SDr/SDg) between them. These results can provide the basis for the development of a specific sensor or sensors system for detecting the ShB disease in rice.

Highlights

Sheath blight (ShB), caused by Rhizoctonia solani AG1-IA, is one of the three major diseases in rice
Their differences were obvious in the original spectrum, which were mainly reflected in the blue edge, green peak, yellow edge, red edge and near-infrared band
The damage caused by ShB is first on leaf sheaths, and on leaf blades when ShB occurs in rice

Summary

Introduction

Sheath blight (ShB), caused by Rhizoctonia solani AG1-IA, is one of the three major diseases in rice. The disease can cause yield losses of 10% to 30%, with most severe loss of up to 50% [1,2,3,4]. The successful control of ShB is crucial to the profitable of rice production [5]. ShB is the key to develop effective management strategies for control of this disease. Sensors 2020, 20, 6243 technology can be a useful tool to quickly, efficiently and accurately detect and monitor the occurrence and development of ShB and other crop diseases and insect pests [6,7,8]. Studies have been conducted using RGB, multispectral and hyperspectral images to detect

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