*Letter* **Recognition of Banana Fusarium Wilt Based on UAV Remote Sensing**

**Huichun Ye 1,2,3, Wenjiang Huang 1,2,3\*, Shanyu Huang <sup>4</sup> , Bei Cui 1,2,3, Yingying Dong 1,2 , Anting Guo 1,5, Yu Ren 1,5 and Yu Jin <sup>6</sup>**


Received: 22 January 2020; Accepted: 12 March 2020; Published: 13 March 2020

**Abstract:** Fusarium wilt (Panama disease) of banana currently threatens banana production areas worldwide. Timely monitoring of Fusarium wilt disease is important for the disease treatment and adjustment of banana planting methods. The objective of this study was to establish a method for identifying the banana regions infested or not infested with Fusarium wilt disease using unmanned aerial vehicle (UAV)-based multispectral imagery. Two experiments were conducted in this study. In experiment 1, 120 sample plots were surveyed, of which 75% were used as modeling dataset for model fitting and the remaining were used as validation dataset 1 (VD1) for validation. In experiment 2, 35 sample plots were surveyed, which were used as validation dataset 2 (VD2) for model validation. An UAV equipped with a five band multispectral camera was used to capture the multispectral imagery. Eight vegetation indices (VIs) related to pigment absorption and plant growth changes were chosen for determining the biophysical and biochemical characteristics of the plants. The binary logistic regression (BLR) method was used to assess the spatial relationships between the VIs and the plants infested or not infested with Fusarium wilt. The results showed that the banana Fusarium wilt disease can be easily identified using the VIs including the green chlorophyll index (CIgreen), red-edge chlorophyll index (CIRE), normalized difference vegetation index (NDVI), and normalized difference red-edge index (NDRE). The fitting overall accuracies of the models were greater than 80%. Among the investigated VIs, the CIRE exhibited the best performance both for the VD1 (OA = 91.7%, Kappa = 0.83) and VD2 (OA = 80.0%, Kappa = 0.59). For the same type of VI, the VIs including a red-edge band obtained a better performance than that excluding a red-edge band. A simulation of imagery with different spatial resolutions (i.e., 0.5-m, 1-m, 2-m, 5-m, and 10-m resolutions) showed that good identification accuracy of Fusarium wilt was obtained when the resolution was higher than 2 m. As the resolution decreased, the identification accuracy of Fusarium wilt showed a decreasing trend. The findings indicate that UAV-based remote sensing with a red-edge band is suitable for identifying banana Fusarium wilt disease. The results of this study provide guidance for detecting the disease and crop planting adjustment.

**Keywords:** Fusarium wilt; crop disease; banana; multispectral remote sensing; UAV

#### **1. Introduction**

Banana (*Musa spp.*) is a widely grown cash crop in the tropics and subtropics. Fusarium wilt (Panama disease) of banana, which is caused by the fungus *Fusarium oxysporum* f. sp. *cubense* race 4 (*Foc* 4), is a serious soilborne fungal disease [1]. This disease currently threatens the banana planting areas worldwide, including areas in Southeast Asia, Jordan, Australia, Lebanon, Pakistan, Mozambique, and Oman [2]. Fusarium wilt disease may have affected approximately 100,000 ha of banana plantations, and it is likely to spread further either through infected plant materials, contaminated soil, or farm machinery or due to flowing water and inappropriate sanitation measures [2]. Externally, the first signs of this disease are wilted banana plants with yellowing of the older leaves around the margins. As the disease advances, the plant leaves finally droop, forming a 'skirt' around the pseudo-stem before falling off. The new leaves may present pale margins and irregular and wrinkled blades [3]. Currently, there are no efficient chemical treatment for Fusarium wilt control. Once a diseased plant has been found, 'timely removal' is the best way to avoid the formation of a disease center [4]. Therefore, timely monitoring of banana Fusarium wilt disease is important for the disease treatment and crop planting adjustment.

Real-time monitoring and identification of crop disease are the basis of timely prevention and control [5]. Traditionally, ground surveys have been the only effective approach to monitor and discriminate crop disease, but these investigations are time-consuming and often very expensive. Remote sensing has become a feasible technology for disease detection and assessment over the last several decades. Diseases that have been detected using remote sensing include rust infection [6–8], Fusarium head blight [9,10], and powdery mildew [9–12] in wheat, bacterial leaf blight in rice [13,14], grey leaf spot in maize [15], and late blight disease and bacterial spot in tomato [16,17]. When plants are infected with diseases, the leaf water, pigment content and internal structure undergo changes, and these biochemical and biophysical changes are also reflected in the spectral characteristics of plants [18]. Many studies have successfully applied sensitive spectral bands or vegetation indices (VIs) to the identification and monitoring of crop diseases in the leaf and canopy scales. Bravo et al. [19] calculated the normalized difference vegetation index (NDVI) using wavelengths of 740–760 nm and 620–640 nm to extract powdery mildew wheat patches. Devadas et al. [20] showed that yellow rust wheat and healthy wheat could be distinguished by the anthocyanin reflection index (ARI). Huang et al. [7] suggested that the red-edge position can be used as an indicator for disease detection. However, spectral bands and VIs exhibit different sensitivity to different diseases and it is necessary to determine which spectral bands and VIs are suitable for the identification of specific diseases.

Satellite-based imagery is an affordable source of data for large-scale agricultural monitoring. There are a few previous studies that have shown successful detection of crop disease using high-resolution satellite multispectral images. For example, Oumar and Mutanga [21] demonstrated the applicability of Worldview-2 image for disease monitoring in a study on the prediction of bronze bug damage in plantation forests. Zhang et al. [22] established a multitemporal, modified soil-adjusted vegetation index for HJ-CCD images, and detected and mapped the outbreak of armyworm. Shi et al. [5] successfully used PlanetScope imagery to identify rice blast, rice dwarf, and glume blight. However, canopy structural characteristics and the biological effects induced by diseases often vary at fine spatial scales. Thus, in practice, the use of satellite-based imagery to monitoring diseases at field or subfield scales must address the constraint that different objects with similar spectral properties are affected by a mixed pixel effect from low-to-moderate resolution satellites (e.g., Landsat OLI-8, Sentinel-2) [5]. Furthermore, the use of high-resolution imageries acquired from satellite platforms is deficient for the long revisit period due to high cost and unfavorable weather conditions. In recent years, the development of unmanned aerial vehicles (UAVs) has provided new imagery acquisition platforms that can collect very high-resolution imagery and data in a short period of time in a cost-effective manner [23]. Therefore, UAVs provide a new technical means from which the in-season growth information of crops can be extracted in a timely and nondestructive manner [24]. Significant progress has been made in crop classification, growth monitoring, and pest and disease identification using

UAV-based multispectral and hyperspectral imagery [23,25,26]. A few studies also applied UAV-based imagery to map spatial patterns of photosynthetic activity in banana plantations [27]. However, studies using UAV-based remote sensing technology to monitor Fusarium wilt of banana are scarce [28].

Moreover, due to the scale effects, the scaling topic has become one of the hotspots in remote sensing research [29]. Although higher spatial resolution images show more landscape details and more accurate estimates [30], due to expensive costs and processing difficulties, it is unnecessary and unrealistic to seek very high-resolution data for the agriculture application. Therefore, it is better to select an appropriate spatial resolution image for agricultural monitoring after considering various factors. In addition, choosing an appropriate method for data analysis is very important, as it directly affects the reliability and accuracy of the results. Many approaches or models have been used to determine bands and features that are sensitive to crop disease detection and discrimination [5,18,31]. Binary logistic regression (BLR) is one of the most commonly used multivariate analysis approaches to describe the relationship between a dependent variable and multiple independent variables, where the dependent variable is a binary variable that indicates whether an event exists [32]. Logistic regression has advantages over linear regression and log-linear linear regression because logistic regression does not need to assume normality [33].

The objectives of this study were to (i) develop an identification method for Fusarium wilt of banana using UAV-based multispectral imagery, (ii) determine the optimal VI for establishing an optimal identification model, and (iii) assess the effect of different image resolution on the identification accuracy of banana Fusarium wilt disease to provide a reference for large-scale applications of satellite-based data.

#### **2. Materials and Methods**

#### *2.1. Study Area*

The experiments were conducted at two experimental locations: The Guangxi site and Hainan site (Figure 1).

The Guangxi site is located in Long'an County, Guangxi Province, China (23◦7'53.2"–23◦8'4.0" N, 107◦43'44.9–07◦44'7.2" E) (Figure 1). The region has a subtropical monsoon climate characterized by year-round sufficient sunshine and rainfall. The mean annual temperature is 20.8–22.4 °C. The average rainfall is 1200 mm a year. The soil type is a Ferralsol according to the IUSS Working Group WRB soil classification system [34]. The field crops were the banana variety "Williams B6." The plant height was about 2.4–3 m, the leaf number was 34–36, and the growth period was 10–12 months. The farm was developed in September 2015 and was harvested for the first time in November 2016. By August 2018 (the time of field investigation in this study), the third generation of bananas was present in the fields. The planting distance was 2.0 m by 2.6 m with a planting density of 1950 plants per hectare. In this study area, more than 40% of banana plants were infected with Fusarium wilt disease of different severity.

The Hainan site is located in Chengmai County, Hainan Province, China (19◦49'4.4"–19◦49'15.8" N, 109◦54'40.0"–109◦54'53.0" E). The region has a tropical monsoon climate characterized by year-round sufficient sunshine and rainfall. The mean annual temperature is 23.1–24.5 °C. The average rainfall is 1750 mm a year. The soil type is a Humic Acrisol according to the IUSS Working Group WRB soil classification system [34]. This experimental site was divided into two fields (left field and right field) along the boundary of the middle road. The left field was planted the banana variety "Baxijiao." The plant height was about 2.6–3.2 m and the growth period was 9–12 months. This field was developed in June 2017 and was harvested for the first time in July 2018. By December 2018, the second generation of bananas was present in the field. The planting distance was 2.0 m by 2.3 m with a planting density of 2100 plants per hectare. In this field, about 10% of banana plants were infected with Fusarium wilt disease of different severity. The right field was developed in August 2018 and the banana variety was "Nantianhuang." The plant height was about 2.5–3.0 m and the growth period was 10–13 months.

The planting distance was the same as the left field. At the time of field investigation in December 2018, there were no plants infected with Fusarium wilt found in this field. In this study, the Guangxi site was used for Fusarium wilt identifying model calibration and validation and the Hainan site was used for model validation.

*Remote Sens.* **2020**, *12*, x FOR PEER REVIEW 4 of 15

**Figure 1.** Location of the experimental sites with the distribution of survey sites in the banana **Figure 1.** Location of the experimental sites with the distribution of survey sites in the banana plantations.

plantations. *2.2. Field Investigation*  In this study, the Guangxi site was used for Fusarium wilt identifying model calibration and validation and the Hainan site was used for model validation.

#### The Guangxi experiment was conducted on 7 August 2018. A total of 120 sample plots were *2.2. Field Investigation*

surveyed to assess the occurrence of banana Fusarium wilt disease as ground truth data (Figure 1). The size of each sample point covered one banana plant. These samples were classified into two categories: Healthy samples (total of 57) and diseased samples (total of 63), representing the occurrence or nonoccurrence of Fusarium wilt as reflected by the external characteristics. The classification standard adopted in this paper was based on the percent of the yellowing leaf area to the total leaf area of the plant. If the percent of the yellowing leaf area to the total leaf area of the plant was less than 1%, the plant was considered to be healthy. Otherwise, it was considered to be diseased. Finally, 75% of the samples were randomly chosen and used as modeling dataset for model fitting, and the remaining 25% were used as validation dataset 1 (VD1) for validation. The Hainan site experiment was conducted on 11 December 2018. The investigation scheme was The Guangxi experiment was conducted on 7 August 2018. A total of 120 sample plots were surveyed to assess the occurrence of banana Fusarium wilt disease as ground truth data (Figure 1). The size of each sample point covered one banana plant. These samples were classified into two categories: Healthy samples (total of 57) and diseased samples (total of 63), representing the occurrence or nonoccurrence of Fusarium wilt as reflected by the external characteristics. The classification standard adopted in this paper was based on the percent of the yellowing leaf area to the total leaf area of the plant. If the percent of the yellowing leaf area to the total leaf area of the plant was less than 1%, the plant was considered to be healthy. Otherwise, it was considered to be diseased. Finally, 75% of the samples were randomly chosen and used as modeling dataset for model fitting, and the remaining 25% were used as validation dataset 1 (VD1) for validation.

consistent with that of Guangxi site experiment. A total of 35 sample plots were investigated, of which 16 were healthy and 19 were diseased. All the sample plots were used as validation dataset 2 (VD2) for model validation. *2.3. UAV Multi-Spectral Imagery Acquisition* The Hainan site experiment was conducted on 11 December 2018. The investigation scheme was consistent with that of Guangxi site experiment. A total of 35 sample plots were investigated, of which 16 were healthy and 19 were diseased. All the sample plots were used as validation dataset 2 (VD2) for model validation.

The surveys were done using a DJI Phantom 4 quadcopter (DJI Innovations, Shenzhen, China). This UAV was equipped with a five-band multispectral camera MicaSense RedEdge MTM (MicaSense, Inc., Seattle, WA, USA), which has five narrow bands: Blue (465–485 nm), green (550–570 nm), red
