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.
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.
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
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.
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.