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.
The temperature of the plant canopy is closely related with its transpirative status, and therefore, its stomatal conductance and cooling capacity. Ear and leaf temperature can provide useful information for monitoring crop water status, irrigation management and yield assessment. Previous studies have shown differences in temperature between ears and leaves, with higher ear temperatures than leaf temperatures observed. By employing a high resolution thermal radiometric camera for proximal imaging, temperature differences can be used for segmentation as well as for temperature estimation. This work uses thermal images taken from above the canopy at between 0.8 and 1m distance. Measurements were acquired after solar noon. The field trials were carried out in three experimental sites and two crop seasons in Spain: Aranjuez (2016/2017), Sevilla (2015/2016) and Valladolid (2016/2017). A set of 24 varieties of durum wheat in two growing conditions, irrigated and rainfed, were used to build the thermal imagery database. The algorithm uses a pipeline system to filter the low temperatures and enhance the local contrast in order to segment the ear regions in each thermal image. Finally, using the full thermal radiometric information, the algorithms provide the temperature for each ear automatically detected. The results show high correlation values between the ear temperatures manually measured (using the thermal camera software) and the ear temperatures automatically measured using an automatic image processing pipeline.
Canopy cover is an important agronomical component for determining grain yield in cereals. Estimates of the canopy cover area of crops may contribute to improving the efficiency of crop management practices and breeding programs. Conventional high resolution RGB cameras can be used to acquire zenithal images taken at ground level or from a UAV (Unmanned Aerial Vehicle). Canopy-image segmentation is complicated in field conditions by numerous factors, including soil, shadows and unexpected objects. Spatial resolution is a key factor for estimating canopy cover area because low spatial resolution may introduce artifacts in the digital image. We propose a comparison of canopy cover segmentation using different spatial resolutions to test the scalability potential of these different techniques. Field trials were carried out during the 2015/2016 crop season in the Arazuri experimental station of INTIA in Navarra, Spain. Three barley genotypes, 10 different N fertilization regimens and three replicates were used in this study. This work uses zenithal RGB images taken from 1 m above the crop and images from the UAV were taken at the intervals of 2 s the during of the flight at distances of 25, 50 and 100 m. Images from the ground were taken at 1 m above the canopy. The CerealScanner plugin for FIJI (Fiji is Just ImageJ) was used to calculate the BreedPix RGB vegetation indices. The comparative results demonstrate the algorithm’s effectiveness in scaling through high correlation values between images with different spatial resolutions taken from the UAV and images taken from the ground.
The number of ears per unit ground area, or ear density, is in most cases the main agronomic yield component of wheat. A fast evaluation of this attribute may contribute to crop monitoring and improve the efficiency of crop management practices as well as breeding programs. Currently, the number of ears is counted manually, which is time consuming. This work uses zenithal RGB images taken from above the crop canopy in natural light and field conditions. Wheat trials were carried out in two sites (Aranjuez and Valladolid, Spain) during the 2014/2015 crop season. A set of 24 varieties of durum wheat in two growing conditions with three dates of measurement were used to create the image database. The algorithm for ear counting uses three steps: (i) Laplacian frequency filter (ii) median filter (iii) Find Maxima. Although the image database was collected at the ground level, we have simulated images at lower resolutions in order to test potential application from cameras with lower resolution, such mobiles phones, action cameras (5 – 12 megapixels), or even aerial platforms (e.g. UAV from 25-50 meters). Images were resized to five different resolutions with no interpolation techniques applied. The results demonstrate high accuracy between the algorithm counts and the manual (image-based) ear counts, higher than 90% in success rate, with a decrease of <1% when images were reduced to a half of its original size, and success rates decreasing by 2.29%, 7.32%, 17.32% and 38.82% for images resized by four, eight, 16 and 32 values, respectively.
Modern textile industry seeks to produce textiles as little defective as possible since the presence of defects can
decrease the final price of products from 45% to 65%. Automated visual inspection (AVI) systems, based on
image analysis, have become an important alternative for replacing traditional inspections methods that involve
human tasks. An AVI system gives the advantage of repeatability when implemented within defined constrains,
offering more objective and reliable results for particular tasks than human inspection.
Costs of automated inspection systems development can be reduced using modular solutions with embedded
systems, in which an important advantage is the low energy consumption. Among the possibilities for developing
embedded systems, the ARM processor has been explored for acquisition, monitoring and simple signal
processing tasks. In a recent approach we have explored the use of the ARM processor for defects detection by
implementing the wavelet transform. However, the computation speed of the preprocessing was not yet sufficient
for real time applications.
In this approach we significantly improve the preprocessing speed of the algorithm, by optimizing matrix
operations, such that it is adequate for a real time application. The system was tested for defect detection
using different defect types. The paper is focused in giving a detailed description of the basis of the algorithm
implementation, such that other algorithms may use of the ARM operations for fast implementations.
Commonly, visual inspection tasks in the textile industry are performed by human experts. The major drawback
of this type of inspection is the human subjectivity, which affects accuracy and repeatability. Objectivity,
accuracy and repeatability can be achieved by analysing visual characteristics of the products using computer
vision. Particularly, automatic real time inspection systems based on texture analysis can be implemented using
Local Binary Pattern (LBP) techniques. A recent variation of the LBP techniques, named Geometric Local
Binary Pattern (GLBP) technique, showed an increase in the performance for detecting small changes of local
texture. In this paper a real time implementation of the algorithm is presented by using a Graphic Processing
Unit (GPU). The LBP and GLBP techniques are compared in terms of speed and accuracy while implemented
on a Central Processing Unit (CPU) and GPU environments. Algorithms are tested for detecting defects in
fabrics as well as for evaluating global deviations of texture, which are due to the degradation of the surface
in carpets. Results show that higher discriminant power between similar textures is obtained when using the
GLBP technique.
Small devices used in our day life are constructed with powerful architectures that can be used for industrial
applications when requiring portability and communication facilities. We present in this paper an example of
the use of an embedded system, the Zeus epic 520 single board computer, for defect detection in textiles using
image processing. We implement the Haar wavelet transform using the embedded visual C++ 4.0 compiler
for Windows CE 5. The algorithm was tested for defect detection using images of fabrics with five types of
defects. An average of 95% in terms of correct defect detection was obtained, achieving a similar performance
than using processors with float point arithmetic calculations.
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