Leaf area index (LAI) is one of the most effective biophysical parameters for characterizing vegetation dynamics and crop productivity. Acquiring a time series of accurately estimated LAI in rice canopies allows to monitor and analyze growth dynamics during the crop season and contributes to a better understanding of photosynthesis, water use, biomass, and yield. Advances in technology platforms and navigation systems have enabled the acquisition of high-resolution images, offering new insights in innovative ways in an era when climate change imposes severe challenges on the agricultural sector. Field trials were conducted during two growing seasons in 2021 and 2022 in the Nataima research center of Agrosavia in El Espinal, Tolima, Colombia. The field trial consisted of three irrigation techniques applied in four Fedearroz 67 rice variety replicates. Multispectral and RGB images were taken from the UAV at 40m (1.83cm/0.49cm GSD), 60m (2.8cm/0.75cm GSD), and 80m (3.77cm/1.0cm GSD) above the crop. Images were then processed using the ViCTool, to compute vegetation indices. In addition, ground-truth LAI was indirectly determined by measuring the fresh and dry weight. Comparative results report significant differences in specific indices and trends for the two growing seasons regarding multispectral vegetation indices (NDRE, NDVI, GNDVI, GVI, SR, OSAVI, and SAVI). For the assessed RGB indices (ExG, GA, and GGA), there were no matching patterns or trends between flight height differences along cycles. These findings also reveal that although significant differences are observed, no greater improvement is seen in the determination coefficients (R2 ) for LAI estimation using linear regression at any height.
This paper compares the speed performance of a set of classic image algorithms for evaluating texture in images by using CUDA programming. We include a summary of the general program mode of CUDA. We select a set of texture algorithms, based on statistical analysis, that allow the use of repetitive functions, such as the Coocurrence Matrix, Haralick features and local binary patterns techniques. The memory allocation time between the host and device memory is not taken into account. The results of this approach show a comparison of the texture algorithms in terms of speed when executed on CPU and GPU processors. The comparison shows that the algorithms can be accelerated more than 40 times when implemented using CUDA environment.
KEYWORDS: Light sources and illumination, Video, Algorithm development, Image segmentation, Feature extraction, Video acceleration, Video processing, Video surveillance, Visualization, Analytical research
Video analysis in real time requires fast and efficient algorithms to extract relevant information from a considerable
number, commonly 25, of frames per second. Furthermore, robust algorithms for outdoor visual scenes may
retrieve correspondent features along the day where a challenge is to deal with lighting changes. Currently, Local
Binary Pattern (LBP) techniques are widely used for extracting features due to their robustness to illumination
changes and the low requirements for implementation. We propose to compute an automatic threshold based on
the distribution of the intensity residuals resulting from the pairwise comparisons when using LBP techniques.
The intensity residuals distribution can be modelled by a Generalized Gaussian Distribution (GGD). In this paper
we compute the adaptive threshold using the parameters of the GGD. We present a CUDA implementation
of our proposed algorithm. We use the LBPSYM technique. Our approach is tested on videos of four different
urban scenes with mobilities captured during day and night. The extracted features can be used in a further
step to determine patterns, identify objects or detect background. However, further research must be conducted
for blurring correction since the scenes at night are commonly blurred due to artificial lighting.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.