Abstract
Leaf area index (LAI) is a fundamental indicator of plant growth status in agronomic and environmental studies. Due to rapid advances in unmanned aerial vehicle (UAV) and sensor technologies, UAV-based remote sensing is emerging as a promising solution for monitoring crop LAI with great flexibility and applicability. This study aimed to determine the feasibility of combining color and texture information derived from UAV-based digital images for estimating LAI of rice (Oryza sativa L.). Rice field trials were conducted at two sites using different nitrogen application rates, varieties, and transplanting methods during 2016 to 2017. Digital images were collected using a consumer-grade UAV after sampling at key growth stages of tillering, stem elongation, panicle initiation and booting. Vegetation color indices (CIs) and grey level co-occurrence matrix-based textures were extracted from mosaicked UAV ortho-images for each plot. As a solution of using indices composed by two different textures, normalized difference texture indices (NDTIs) were calculated by two randomly selected textures. The relationships between rice LAIs and each calculated index were then compared using simple linear regression. Multivariate regression models with different input sets were further used to test the potential of combining CIs with various textures for rice LAI estimation. The results revealed that the visible atmospherically resistant index (VARI) based on three visible bands and the NDTI based on the mean textures derived from the red and green bands were the best for LAI retrieval in the CI and NDTI groups, respectively. Independent accuracy assessment showed that random forest (RF) exhibited the best predictive performance when combining CI and texture inputs (R2 = 0.84, RMSE = 0.87, MAE = 0.69). This study introduces a promising solution of combining color indices and textures from UAV-based digital imagery for rice LAI estimation. Future studies are needed on finding the best operation mode, suitable ground resolution, and optimal predictive methods for practical applications.
Highlights
Being a fundamental variable in agronomic and environmental studies, leaf area index (LAI) is commonly used as a crucial biophysical indicator of vegetation [1] for plant photosynthesis [2], productivity [3], and water utilization [4]
Lu et al [20] found that the integrated approach consisting of both unmanned aerial vehicle (UAV) RGB imagery and point cloud data improved the predictive performance of wheat biomass
An increasing trend of LAI was shown in both the training set and test set at the four growth stages covering most of the growing cycle of rice
Summary
Being a fundamental variable in agronomic and environmental studies, leaf area index (LAI) is commonly used as a crucial biophysical indicator of vegetation [1] for plant photosynthesis [2], productivity [3], and water utilization [4]. LAI is a useful indicator for crop growth diagnosis, biomass estimation and yield prediction in practical use of precision agriculture [5,6]. Multispectral or hyperspectral images from satellites or airborne platforms have shown great capabilities to estimate forest and crop LAI based on vegetation indices (VIs) or degree of biomass coverage at regional and global scales [9,10,11]. For applications in small areas, many ground-based non-imaging sensors, such as GreenSeeker (Trimble Navigation Limited, Sunnyvale, CA, USA) and Crop Circle series (Holland Scientific, Lincoln, NE, USA), have been used on canopy scale to estimate LAI, nitrogen status, and predict crop yield [12,13]. The overall cost of using these ground-based sensors needs to be evaluated due to the high labor input and the inefficient use of these sensors [14]
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