The infection of Apple mosaic virus (ApMV) can severely damage the cellular structure of apple leaves, leading to a decrease in leaf chlorophyll content (LCC) and reduced fruit yield. In this study, we propose a novel method that utilizes hyperspectral imaging (HSI) technology to non-destructively monitor ApMV-infected apple leaves and predict LCC as a quantitative indicator of disease severity. LCC data were collected from 360 ApMV-infected leaves, and optimal wavelengths were selected using competitive adaptive reweighted sampling algorithms. A high-precision LCC inversion model was constructed based on Boosting and Stacking strategies, with a validation set Rv2 of 0.9644, outperforming traditional ensemble learning models. The model was used to invert the LCC distribution image and calculate the average and coefficient of variation (CV) of LCC for each leaf. Our findings indicate that the average and CV of LCC were highly correlated with disease severity, and their combination with sensitive wavelengths enabled the accurate identification of disease severity (validation set overall accuracy = 98.89%). Our approach considers the role of plant chemical composition and provides a comprehensive evaluation of disease severity at the leaf scale. Overall, our study presents an effective way to monitor and evaluate the health status of apple leaves, offering a quantifiable index of disease severity that can aid in disease prevention and control.
Leaf chlorophyll content (LCC) is a crucial indicator of nutrition in apple trees and can be applied to assess their growth status. Hyperspectral data can provide an important means for detecting the LCC in apple trees. In this study, hyperspectral data and the measured LCC were obtained. The original spectrum (OR) was pretreated using some spectral transformations. Feature bands were selected based on the competitive adaptive reweighted sampling (CARS) algorithm, random frog (RF) algorithm, elastic net (EN) algorithm, and the EN-RF and EN-CARS algorithms. Partial least squares regression (PLSR), random forest regression (RFR), and the CatBoost algorithm were used before and after grid search parameter optimization to estimate the LCC. The results revealed the following: (1) The spectrum after second derivative (SD) transformation had the highest correlation with LCC (–0.929); moreover, the SD-based model produced the highest accuracy, making SD an effective spectrum pretreatment method for apple tree LCC estimation. (2) Compared with the single band selection algorithm, the EN-RF algorithm had a better dimension reduction effect, and the modeling accuracy was generally higher. (3) CatBoost after grid search optimization had the best estimation effect, and the validation set of the SD-EN-CARS-CatBoost model after parameter optimization had the highest estimation accuracy, with the determination coefficient (R2), root mean square error (RMSE), and relative prediction deviation (RPD) reaching 0.923, 2.472, and 3.64, respectively. As such, the optimized SD-EN-CARS-CatBoost model, with its high accuracy and reliability, can be used to monitor the growth of apple trees, support the intelligent management of apple orchards, and facilitate the economic development of the fruit industry.
The estimation of anthocyanin (Anth) content is very important for observing the physiological state of plants under environmental stress. The objective of this study was to estimate the Anth of maize leaves at different growth stages based on remote sensing methods. In this study, the hyperspectral reflectance and the corresponding Anth of maize leaves were measured at the critical growth stages of nodulation, tasseling, lactation, and finishing of maize. First-order differential spectra (FD) were derived from the original spectra (OS). First, the spectral parameters highly correlated with Anth were selected. A total of two sensitive bands (Rλ), five classical vegetation indices (VIS), and six optimized vegetation indices (VIC) were selected from the original and first-order spectra. Then, univariate regression models for Anth estimation (Anth-UR models) and multivariate regression models for estimating anthocyanins (Anth-MR models) were constructed based on these parameters at different growth stages of maize. It was shown that the first-order spectral conversion could effectively improve the correlation between Rλ, VIC, and Anth, and VIC are usually more sensitive to Anth than VIS. In addition, the overall performance of Anth-MR models was better than that of Anth-UR models. Among them, Anth-MR models with the combination of three types of spectral parameters (FD(Rλ) + OS_VIC + FD_VIC/VIS) as inputs had the best overall performance. Moreover, different growth stages had an impact on the Anth estimation models, with tasseling and lactation stages showing better results. The best-performing Anth-MR models for these two growth stages were as follows. For the tasseling stage, the best model was the FD(Rλ) + OS_VIC + VIS-based SVM model, with an R2 of 0.868, RMSE of 0.007, and RPD of 2.19. For the lactation stage, the best-performing model was the FD(Rλ) + OS_VIC + FD_VIC-based RF model, with an R2 of 0.797, RMSE of 0.007, and RPD of 2.24. These results will provide a scientific basis for better monitoring of Anth using remote sensing hyperspectral techniques.
Mosaic of apple leaves is a major disease that reduces the yield and quality of apples, and monitoring for the disease allows for its timely control. However, few studies have investigated the status of apple pests and diseases, especially mosaic diseases, using hyperspectral imaging technology. Here, hyperspectral images of healthy and infected apple leaves were obtained using a near-ground imaging high spectrometer and the anthocyanin content was measured simultaneously. The spectral differences between the healthy and infected leaves were analyzed. The content of anthocyanin in the leaves was estimated by the optimal model to determine the degree of apple mosaic disease. The leaves exhibited stronger reflectance at a range of 500–560 nm as the degree of disease increased. The correlation between the spectral reflectance processed by the Gaussian1 wavelet transform and anthocyanin was significantly improved compared to the corresponding correlation results with the original spectrum. The VPs-XGBoost anthocyanin estimation model performed the best, which was sufficient to monitor the degree of the disease. The findings provide theoretical support for the quantitative estimation of leaf anthocyanin content by remote sensing to monitor the degree of disease; they lay the foundation for large-scale monitoring of the degree of apple mosaic disease by remote sensing.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.