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Pest and diseases have seriously threatened to the yield and quality of citrus. This study aims to explore the effect of citrus diseases on SPAD value using identification model by near-infrared spectroscopy with chemometrics. Leaf spectra and physicochemical indexes were further determined to detect the citrus diseases. The NIR spectra of citrus leaves with normal, canker and emery derma were obtained to measure the physicochemical indices by portable NIR spectrometer. The spectrum analysis showed that there was a reflection peak in the leaves near 553 nm, indicating the reflection peak after the chlorophyll saturated the high-frequency absorption of light. The maximum reflection peak of normal leaves was about 0.21, whereas, the lowest was about 0.13 in the leaves with the sand skin disease. The reflectance of normal leaves was higher than that of diseased leaves at 750-900 nm. The reason was that the reflectance decreased to enlarge the leaf cell cavities under disease stress. The variance was analyzed for two physical and chemical indexes. There were significant differences in the SPAD value indexes of the three types of citrus leaves, while no significant difference was found in fresh quality indexes. A qualitative and quantitative model of citrus diseases was also established to clarify the relationship with leaf spectrum. According to the ratio of 2:1, 212 blades were randomly divided into two dataset with 145 samples in training set and 67 samples in verification set. The SPA and PCA were used to screen the spectral variables. 17 and 20 optimal variables were selected to combine the RF and LWPLS. The comparison showed that the best detection was achieved in the qualitative model of LWPLS for three types of disease leaves using 401 variables of the original spectrum, with the accuracy of 94.03%. While the low accuracy of 77.61% was found in the qualitative model of RF for three types of disease leaves using SPA screening 17 variables. The main reason was that a few sensitive variables were eliminated, although the characteristic variables represented the most information of citrus leaves. A numerical model was also constructed to further verify whether the leaf diseases affected the SPAD value of normal and diseased leaves. The best performance was achieved in the LWPLS model in the normal blade SPAD value analysis with 401 spectral variables as the input. The RMSEP of LWPLS quantitative model for normal, canker and emery was 4.46%, the model had better prediction accuracy for SPAD value of leaves, R² and RPD were 0.93 and 2.19, respectively. NIR spectroscopy with chemometrics was feasible to identify the citrus diseases on physicochemical indexes of leaves. The finding can provide an important reference for the real-time field detection of citrus diseases. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle (UAV) remote sensing can be widely used to capture large-scale high overlapping and high spatial resolution images from the ground in a low-cost way. However, the UAV Red-Green-Blue (RGB) images are usually obtained, because the UAV sensors can only contain the wavebands with the visible light range, such as the red, green, and blue. It is difficult to extract the feature information from the UAV RGB images because of the fewer wavebands. Taking the UAV RGB orthophoto image as the research object, an image extraction was proposed to integrate the point cloud matching with the Hue-Saturation-Intensity (HSI) color component. Firstly, the high overlapping ground images were acquired by UAV, thereby constructing the point cloud matching using dense matching, where a digital surface model was generated. Then, the ground points and non-ground points in the study area were obtained by a cloth simulation filtering. Specifically, the ground points were interpolated to generate the digital elevation model. As such, a normalized digital surface model was obtained via the difference between the digital surface model and digital elevation model. Secondly, a saturation-red ratio index was established to extract the bare land using the color components of HSI color space and visible light bands. A bare land extraction was then carried out to verify the model. Thirdly, the expansion index was constructed by multiplying the formula of saturation-red ratio index by the standard deviation of the red band to extract the water with the sediment. Finally, two models and three indexes were achieved to classify the UAV RGB orthophoto images in the study area, including the digital elevation model, the normalized digital surface model, the visible-band difference vegetation index, the saturation-red ratio index, and the expansion index. Afterwards, an experiment was then carried out to verify the extraction model. The results showed that the saturation-red ratio index presented a higher accuracy in the bare land extraction from the UAV RGB images. The normalized digital surface model performed better to obtain the key feature that distinguished from the trees and grasslands using the matching of point cloud interpolation. Furthermore, the river and bare land were successfully extracted, using the saturation-red ratio and the expansion index under the HSI color components, while the digital elevation model features that obtained from the point cloud matching. The overall accuracy of the rule-based classification was 91.11%, where the Kappa coefficient was 0.895. In the classification, the highest producer’s accuracy and user’s accuracy were the buildings and rivers, which reached 96.25% and 99.15%, respectively. Moreover, there was an outstanding elevation difference in the high-precision digital elevation model, digital surface model, and normalized digital surface model. Additionally, the saturation-red ratio and expansion indexes were composed of the visible light band and the color component of saturation, particularly for the extraction of bare land and sand water. Consequently, it can be feasible to combine the point cloud matching and the color components of the HSI color space in the extraction of UAV RGB orthophoto image information. [ABSTRACT FROM AUTHOR]