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.
Highlights
unmanned aerial vehicle (UAV) remote sensing (RS) plays an outstanding role in precision agriculture due to its convenient, fast, and accurate acquisition of surface information [1]
Correlation analysis was conducted between leaf area index (LAI) of each growth period and 41 parameters which contained 17 spectral indices and 24 texture features constructed by UAV RGB
The variable set α was composed of 10 spectral features, and 16 texture features formed the variable set β in initial flowering stage (IF)
Summary
UAV remote sensing (RS) plays an outstanding role in precision agriculture due to its convenient, fast, and accurate acquisition of surface information [1]. Some additional methods of crop growth monitoring have gradually emerged lately owing to the enhanced affordability and accessibility of the drones with multispectral imaging, especially digital cameras [4]. Several studies have indicated that drones equipped with digital cameras played an irreplaceable role in crop growth monitoring, which was commonly represented using parameters of growth condition such as leaf area index [5], leaf chlorophyll content [6], biomass [7], yield [8,9], leaf nitrogen content [10], nitrogen. Real-time dynamic monitoring of LAI is of great significance to crop growth diagnosis and management regulation
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