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 (CI_green), red-edge chlorophyll index (CI_RE), 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 CI_RE 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.

The vertical leaf nitrogen (N) distribution in the crop canopy is considered to be an important adaptive response of crop growth and production. Remote sensing has been widely applied for the determination of a crop’s N status. Some studies have also focused on estimating the vertical leaf N distribution in the crop canopy, but these analyses have rarely considered the plant geometry and its influences on the remote estimation of the N vertical distribution in the crop canopy. In this study, field experiments with three types of maize (Zea mays L.) plant geometry (i.e., horizontal type, intermediate type, and upright type) were conducted to demonstrate how the maize plant geometry influences the remote estimation of N distribution in the vertical canopy (i.e., upper layer, middle layer, and bottom layer) at different growth stages. The results revealed that there were significant differences among the three maize plant geometry types in terms of canopy architecture, vertical distribution of leaf N density (LND, g m⁻²), and the LND estimates in the leaves of different layers based on canopy hyperspectral reflectance measurements. The upright leaf variety had the highest correlation between the lower-layer LND (R² = 0.52) and the best simple ratio (SR) index (736, 812), and this index performed well for estimating the upper (R² = 0.50) and middle (R² = 0.60) layer LND. However, for the intermediate leaf variety, only 25% of the variation in the lower-layer LND was explained by the best SR index (721, 935). The horizontal leaf variety showed little spectral sensitivity to the lower-layer LND. In addition, the growth stages also affected the remote detection of the lower leaf N status of the canopy, because the canopy reflectance was dominated by the biomass before the 12th leaf stage and by the plant N after this stage. Therefore, we can conclude that a more accurate estimation of the N vertical distribution in the canopy is obtained by canopy hyperspectral reflectance when the maize plants have more upright leaves.

Wheat grain protein content (GPC) is a key component when evaluating wheat nutrition. It is also important to determine wheat GPC before harvest for agricultural and food process enterprises in order to optimize the wheat grading process. Wheat GPC across a field is spatially variable due to the inherent variability of soil properties and position in the landscape. The objectives of this field study were: (i) to assess the spatial and temporal variability of wheat nitrogen (N) attributes related to the grain quality of winter wheat production through canopy fluorescence sensor measurements; and (ii) to examine the influence of spatial variability of soil N and moisture across different growth stages on the wheat grain quality. A geostatistical approach was used to analyze data collected from 110 georeferenced locations. In particular, Ordinary Kriging Analysis (OKA) was used to produce maps of wheat GPC, GPC yield, and wheat canopy fluorescence parameters, including simple florescence ratio and Nitrogen Balance Indices (NBI). Soil Nitrate-Nitrogen (NO₃-N) content and soil Time Domain Reflectometry (TDR) value in the study field were also interpolated through the OKA method. The fluorescence parameter maps, soil NO₃-N and soil TDR maps obtained from the OKA output were compared with the wheat GPC and GPC yield maps in order to assess their relationships. The results of this study indicate that the NBI spatial variability map in the late stage of wheat growth can be used to distinguish areas that produce higher GPC.