conductance, photosynthesis, chlorophyll content, which could be related to
Nitrogen deficiencies and by consequence to photosynthetic rate (Jones 1999;
Jones et al. 2002; Netto et al. 2005). In the food industry, the image processing has
been employed to determine the quality of a product based in several variables
such as texture, size, shape, and color principally, these variables could be measure
by experts but, the assessments will be subjective because of the judgments variation. Other application of image processing in the agriculture is the identification
of weeds, which is other factor that provoke looseness in cultivated crops, and
several techniques have been developed with the objective of segmenting automatically the weed, soils, and plants. Visible phenomena in plants and fruit have
the possibility of being monitoring by using image processing.
13.2 Image Acquisition Process
There exists several algorithms to segment the image into a region of interest and
region of no interest; they could be simple or difficult depending of the complexity
degree of the image. The features of interest in agriculture and food industry
generally are those related to texture, color, shape, and size (Jackman and Sun
2013; Gomes and Leta 2012). In the agriculture, the techniques employed are
focused principally to physiological variables, symptoms detection and weeds
segmentation, among others. In food industry, the objective is majorly focused to
the quality of product and analyzes several techniques.
13.2.1 Image Processing in Food Industry
In the last decade, the use of technology in food industry has been increased
greatly, principally due to the high demanding market that day by day requires and
demand products of high quality. Also, the market restriction in the last few years
has pursued the use of technology in food production (Gomes and Leta 2012). The
systems of visual inspections normally consist of a light source, a camera, commonly a couple charge device (CCD) for capturing the image and a computational
system for extracting features of images. Commonly, these kind of systems are
used in production lines, where human activity is repetitive and the products need
to be manufactured very rapidly, so that, decision making must be based on fast
and accurate assessments during the overall process. The advantage of having this
systems is that they offers repeatability and accuracy by eliminating subjectivity,
tiredness, slowness and the absorbance of the cost related to human inspection
(Gomes and Leta 2012). The system captures the image by using a camera,
scanner, videos, etc. Subsequently, it converts the image into digital format and
after this, a pre-processing stage is generally required to highlight the region of
interest and to remove noise that could interfere at the time of extracting important
13 Instrumentation and Control to Improve the Crop Yield
365
Nitrogen deficiencies and by consequence to photosynthetic rate (Jones 1999;
Jones et al. 2002; Netto et al. 2005). In the food industry, the image processing has
been employed to determine the quality of a product based in several variables
such as texture, size, shape, and color principally, these variables could be measure
by experts but, the assessments will be subjective because of the judgments variation. Other application of image processing in the agriculture is the identification
of weeds, which is other factor that provoke looseness in cultivated crops, and
several techniques have been developed with the objective of segmenting automatically the weed, soils, and plants. Visible phenomena in plants and fruit have
the possibility of being monitoring by using image processing.
13.2 Image Acquisition Process
There exists several algorithms to segment the image into a region of interest and
region of no interest; they could be simple or difficult depending of the complexity
degree of the image. The features of interest in agriculture and food industry
generally are those related to texture, color, shape, and size (Jackman and Sun
2013; Gomes and Leta 2012). In the agriculture, the techniques employed are
focused principally to physiological variables, symptoms detection and weeds
segmentation, among others. In food industry, the objective is majorly focused to
the quality of product and analyzes several techniques.
13.2.1 Image Processing in Food Industry
In the last decade, the use of technology in food industry has been increased
greatly, principally due to the high demanding market that day by day requires and
demand products of high quality. Also, the market restriction in the last few years
has pursued the use of technology in food production (Gomes and Leta 2012). The
systems of visual inspections normally consist of a light source, a camera, commonly a couple charge device (CCD) for capturing the image and a computational
system for extracting features of images. Commonly, these kind of systems are
used in production lines, where human activity is repetitive and the products need
to be manufactured very rapidly, so that, decision making must be based on fast
and accurate assessments during the overall process. The advantage of having this
systems is that they offers repeatability and accuracy by eliminating subjectivity,
tiredness, slowness and the absorbance of the cost related to human inspection
(Gomes and Leta 2012). The system captures the image by using a camera,
scanner, videos, etc. Subsequently, it converts the image into digital format and
after this, a pre-processing stage is generally required to highlight the region of
interest and to remove noise that could interfere at the time of extracting important
13 Instrumentation and Control to Improve the Crop Yield
365