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Open AccessLetter

Recognition of Banana Fusarium Wilt Based on UAV Remote Sensing

by Huichun Ye 1,2,3, Wenjiang Huang 1,2,3,*, Shanyu Huang 4, Bei Cui 1,2,3, Yingying Dong 1,2, Anting Guo 1,5, Yu Ren 1,5 and Yu Jin 6
1
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
2
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
3
Key Laboratory of Earth Observation of Hainan Province, Sanya 572029, China
4
Chinese Academy of Agricultural Engineering Planning & Design, Beijing 100125, China
5
University of Chinese Academy of Sciences, Beijing 100049, China
6
School of Electronics and Information Engineering, Anhui University, Hefei 230601, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(6), 938; https://doi.org/10.3390/rs12060938
Received: 22 January 2020 / Revised: 9 March 2020 / Accepted: 12 March 2020 / Published: 13 March 2020
(This article belongs to the Special Issue UAVs for Vegetation Monitoring)
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. View Full-Text
Keywords: Fusarium wilt; crop disease; banana; multispectral remote sensing; UAV Fusarium wilt; crop disease; banana; multispectral remote sensing; UAV
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MDPI and ACS Style

Ye, H.; Huang, W.; Huang, S.; Cui, B.; Dong, Y.; Guo, A.; Ren, Y.; Jin, Y. Recognition of Banana Fusarium Wilt Based on UAV Remote Sensing. Remote Sens. 2020, 12, 938.

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