Combining Spectral and Textural Information from UAV RGB Images for Leaf Area Index Monitoring in Kiwifruit Orchard

Abstract

The use of a fast and accurate unmanned aerial vehicle (UAV) digital camera platform to estimate leaf area index (LAI) of kiwifruit orchard is of great significance for growth, yield estimation, and field management. LAI, as an ideal parameter for estimating vegetation growth, plays a significant role in reflecting crop physiological process and ecosystem function. At present, LAI estimation mainly focuses on winter wheat, corn, soybean, and other food crops; in addition, LAI on forest research is also predominant, but there are few studies on the application of orchards such as kiwifruit. Concerning this study, high-resolution UAV images of three growth stages of kiwifruit orchard were acquired from May to July 2021. The extracted significantly correlated spectral and textural parameters were used to construct univariate and multivariate regression models with LAI measured for corresponding growth stages. The optimal model was selected for LAI estimation and mapping by comparing the stepwise regression (SWR) and random forest regression (RFR). Results showed the model combining texture features was superior to that only based on spectral indices for the prediction accuracy of the modeling set, with the R2 of 0.947 and 0.765, RMSE of 0.048 and 0.102, and nRMSE of 7.99% and 16.81%, respectively. Moreover, the RFR model (R2 = 0.972, RMSE = 0.035, nRMSE = 5.80%) exhibited the best accuracy in estimating LAI, followed by the SWR model (R2 = 0.765, RMSE = 0.102, nRMSE = 16.81%) and univariate linear regression model (R2 = 0.736, RMSE = 0.108, nRMSE = 17.84%). It was concluded that the estimation method based on UAV spectral parameters combined with texture features can provide an effective method for kiwifruit growth process monitoring. It is expected to provide scientific guidance and practical methods for the kiwifruit management in the field for low-cost UAV remote sensing technology to realize large area and high-quality monitoring of kiwifruit growth, thus providing a theoretical basis for kiwifruit growth investigation.

Estimate leaf area index and leaf chlorophyll content in winter-wheat using image texture and vegetation indices derived from multi-temporal RGB images

“…Zha et al (2020) also found that RF was superior to SVR, MLR and ANN in predicting nitrogen content in rice using spectral features. These conclusions are consistent with previous prediction studies of other crops traits (Lee et al, 2020;Zhang et al, 2022c). The RF model, which is a popular ensemble learning algorithm, utilizes bootstrap resampling to extract multiple sample sets from the original sample, forming sample subsets.…”

“…In assessing aboveground biomass (AGB) in winter oilseed rape fields, Liu et al (Liu et al, 2019) found single texture indices less accurate than vegetation indices (VIs). Machine learning algorithms have been demonstrated to possess robust capabilities for handling non-linear and multivariate regression between remote sensing data and vegetation physiological and biochemical parameters (Ma et al, 2022;Zhang et al, 2022c). The RF model performed better than the SVR model in the three machine learning models, while the BP model was the weakest in all three types of data inputs.…”

Toward Multi-Stage Phenotyping of Soybean with Multimodal UAV Sensor Data: A Comparison of Machine Learning Approaches for Leaf Area Index Estimation

“…Yu et al [88] used both multivariate and univariate algorithms to construct prediction models of LAI, and found that the multivariate models outperformed the univariate models in estimating LAI in a forest. Similar results were also found in the studies of LAI in wheat [75,76], kiwifruit orchard [89] and soybean in this study. The possible reason for this might be that multivariate algorithms can include more variables with explanatory power and can reduce the possibility of omitting variable bias.…”

Optimizing window size and directional parameters of GLCM texture features for estimating rice AGB based on UAVs multispectral imagery

“…Furthermore, as rice is a row-cropped crop with an apparent spatial direction, the directional selection may impinge upon the monitoring performance of TFs in assessing AGB. However, previous studies have primarily relied on default texture parameters setting (such as a 3x3 window size and diagonal direction at 45°) ( Zheng et al., 2018 ; Li et al., 2019 ; Zheng et al., 2019 ; Xu L. et al., 2022 ; Zhang D. et al., 2022 ; Zhang Y. et al., 2022 ;) or a directionless approach (by averaging multiple directional TFs to eliminate the directional effect) ( Wang F. et al., 2021 ; Liu et al., 2022a ; Xu T. et al., 2022 ) when extracting GLCM-based TFs. A quantitative analysis of the impact of TFs derived from different window sizes and direction parameters for crop AGB estimation has been omitted from these studies.…”