Recognition of Banana Fusarium Wilt Based on UAV Remote Sensing

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.

Modified Red Blue Vegetation Index for Chlorophyll Estimation and Yield Prediction of Maize from Visible Images Captured by UAV

“…All these techniques have both pros and cons. The DS method is quite precise and reliable, but it has disadvantages for it requires repeated contiguous measurements of samples in the field that are time-consuming, and it was found not to be suitable for estimating the chlorophyll contents for a large area [ 25 , 26 ]. SM are another optional way of precisely assessing the chlorophyll contents at a high resolution, but these methods also rely on a high quality of ground sampling data.…”

Experimental Evaluation and Consistency Comparison of UAV Multispectral Minisensors

“…Because of the unique response characteristics of vegetation in the near-infrared band, most vegetation indices (such as the normalized vegetation index [14] and the soil-adjusted vegetation index) are currently based on a combination of visible light and near-infrared bands [15]. At present, there are a variety of UAV-based multispectral minisensors on the market that can be used for vegetation monitoring [16][17][18][19][20][21][22][23] and can be selected according to the different needs of users. To make the VI products obtained from different sensors at different times comparable, the digital number (DN) of the collected image data is usually converted into reflectance, and then the reflectance is used to calculate the vegetation index [24].…”

Efficacy of PCR-bassed techniqes and remote sensing technology for detecting potato brown rot infection caused by R. solanacearum

“…Also, it could help farmers and producers to be more focalized and timely response to intra-field and intra-crop variations such as the outbreak of disease. For clarification , remote sensing has been used to detect many diseases that affect plants, for example: phytophthora foot rot in citrus trees (Fletcher et al,2001) ;late blight in tomatoes (Zhang et al,2005); yellow rust in wheat (Huang et al,2007); citrus greening disease ( Kumar et al,2012); downy mildew in cucumber (Tian & Zhang,2012) ; powdery mildew in winter wheat ( Yuan et al,2014) ; grapevine leafroll disease -GLD (MacDonald,2016); yellow leaf curl disease in tomato leaves (Lu et al,2018) and fusarium wilt in Banana ( Ye et al,2020).…”