**1. Introduction**

As one of the most important food crops in the world, maize covered the largest area planted in China from 2015 to 2019, with more than 40 million hectares planted each year [1]. Therefore, guaranteeing healthy growths of maize is crucial for ensuring food security and achieving sustainable agricultural development worldwide [2]. However, maize dwarf mosaic virus (MDMV) infection adversely affects the growth and development of this crop, and it has become one of the major destructive diseases in the world, including China [3,4]. In 1962, MDMV was first detected in Ohio, USA, and had spread throughout the state by 1964, damaging 5 million corn plants in a dozen counties [5]. In 1965, Janson named the pathogen "maize dwarf mosaic virus" [6]. In 1968, MDMV was reported for the first time on a large scale in Xinxiang, Huixian, and other regions in the Henan Province of China, resulting in the loss of nearly 25 million kilograms of grain. In the 1980s, the disease was effectively prevented and controlled thanks to the promotion of specific resistant varieties and agronomic cultivation measures. However, since the 1990s, due to the increased acreage of MDMV-susceptible varieties, MDMV has become prevalent once again, occurring very frequently in China and causing substantial crop losses. Therefore, prompt and accurate identification of MDMV is crucial for proper field managemen<sup>t</sup> in

**Citation:** Luo, L.; Chang, Q.; Wang, Q.; Huang, Y. Identification and Severity Monitoring of Maize Dwarf Mosaic Virus Infection Based on Hyperspectral Measurements. *Remote Sens.* **2021**, *13*, 4560. https://doi.org/ 10.3390/rs13224560

Academic Editor: Xanthoula Eirini Pantazi

Received: 3 October 2021 Accepted: 9 November 2021 Published: 13 November 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

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order to prevent and control disease spread, and regular yield assessments are essential to devise marketing plans [7–9].

MDMV infection can occur throughout the maize growth period. At the early stages of disease, many elliptical chlorotic spots or markings appear near the veins at the base of the heart lobe, arranged along the veins into intermittent strips of varying lengths. With further progression of the disease, wide chlorotic stripes formed on the leaves, particularly on the young ones [2]. After contracting the disease, the chlorophyll (Chl) content of leaves reduces, turning them yellow. In some cases, the symptoms start to develop from the tip and edge of the leaves, appearing as red-purple stripes; eventually, the entire leaf becomes red and dries. However, these red-purple streaks are mainly a result of color rendering by high concentrations of anthocyanins (Anth) in the infected leaves following Chl degradation [10,11]. As an important pigment, Anth confers all colors, except green, in plants and is sensitive to environmental and biological stresses [12–15]. In addition, pathogen infection can induce anthocyanin biosynthesis in plants, and the more severe the pathogen infection, the stronger the induction ability [16]. Ludmerszki et al. observed the fourth leaf of MDMV-infected maize and found that the observed leaf gradually turned red over time and that the content of anthocyanin in MDMV-infected leaves increased while the content of chlorophyll decreased by using fluorescence technology [17]. Singh and Sharma reported in 1998 that anthocyanins and phenols increase *Chkahao* resistance to a variety of common rice diseases (e.g., root rot, narrow brown spot, stem rot, false smut, bacterial leaf streaks, and bacterial leaf blight) and rice pests (e.g., stem borer, rice bug, green horned caterpillar, and rice skipper) [18]. Fasahat et al. reported in 2012 that a Malaysian colored rice, *Oryza Rufipogon*, containing anthocyanin pigment was highly resistant to bacterial leaf blight and brown plant hopper [19]. Therefore, we used red leaf Anth as a measure to indicate the severity of MDMV infection given the close association between Anth and plant disease.

Traditional MDMV monitoring methods include field observations and laboratory measurements, which are time-consuming and expensive. In addition, chemical methods are destructive, and they cannot reflect progressive changes in the disease status in the same leaf over time [20]. In contrast, remote sensing (RS) is a non-destructive technique for rapid monitoring at different scales. Moreover, owing to its high spatial resolution, hyperspectral technology can identify invisible symptoms reflecting the physiological status of plants at the initial stages of disease and has been widely used in crop pest and disease detection in recent years [21–23]. For instance, Mirik et al. classified Landsat 5 Thematic Mapper (TM) images of two cities in Texas from 2006 to 2008 by using the maximum likelihood method and achieved an overall classification accuracy of 89.47–99.07% for wheat streak mosaic virus [24]. Furthermore, Camino et al. coupled a spatial spread model with an RSdriven support vector for estimating the probability of *Xylella fastidiosa* (XF) infection and obtained highly accurate predictions of the spatial distribution of plant disease in almond trees [25]. Martins et al. conducted field surveys and small-format aerial photography (SFAP) with different cameras to obtain visible and near-infrared images and estimated the spatial distribution of ink disease in Northern Portugal during 1995–2004 by using a geostatistical method [26]. Liu et al. collected rice reflectance spectra in the field and laboratory and estimated the severity of brown rice spot disease based on the reflectance ratio [27]. These previous studies monitored plant diseases at different scales based on satellite, unmanned aerial vehicle (UAV), and near-ground platforms, achieving satisfactory results. Simultaneously, other plant diseases, including wheat stripe rust, rice spikelet rot disease, and maize stripe rust, have also been studied by using RS technology [28–30]. However, only Beverly et al. compared the spectra (400–2700) of healthy, MDMV-infected, and *Helminthosporium maydis*-infected maize leaves, laying the foundation for RS-based research on MDMV [31], and to our best knowledge, there have been no further studies on MDMV using RS technology. Therefore, RS-based research on MDMV is of paramount importance.

Furthermore, reliable physical and empirical models of leaf reflectance and a range of physiological parameters, such as leaf water content, leaf area, and pigment content (including Anth), have been established [32,33]. Therefore, we can indirectly assess the severity of MDMV infection by establishing the relationship between the reflectance spectrum and Anth. To this end, by analyzing the spectral characteristics of infected leaves through RS, we aimed to build a suitable model for the detection and monitoring of MDMV for minimizing its adverse effects and ensuring high crop quality and yield.
