Image processing techniques are used in agro-based industries for quality control, product grading, and automation of tasks like packaging inspection and labeling. Key applications of image processing include examining visual features like color, texture, size, and shape to evaluate physical properties of food. While current systems are successful under constrained conditions, the field is still in its infancy. Future areas of focus include developing more robust 3D systems with color processing and artificial intelligence to handle biological variability and increase acceptability across multiple applications. Barriers to adoption include the need for specialized hardware and software as well as high system costs compared to benefits.

1. IMAGE PROCESSING
IN AGRO-BASED
INDUSTRIES
Sandeep Pawar (143350016)

2. Automatio
n
Control
System
Machine
Vision
Image
Processing
2

3. Image processing in industries
Source: Gunasekaran S, Vol. IV- Automation of food
processing
3

4. 4
Source: Clarence W., Research Laboratory for fish processing automation

5. Image processing steps
Image enhancement
Image segmentation
Feature extraction
Template matching
Pattern recognition
5

6. Image processing in food
industries6
Physical properties of food- visual features
 Colour
 Most influential attribute
 Visual inspection : important part of quality control
 Texture
 Soft, hard, crispy, powdery, spongy
 Size and Shape
 Appearance, mouth feel
 Mass and volume

7. Product grading
7
Source: M.Omid et al, 2010,
Journal of food engineering

8. Classification by texture
analysis8
Source: A.
Khoshroo et al,
Classification of
Pomegranate Fruit
using Texture
Analysis of MR
Images (2009)

9. Image processing in packaging
9
 Package inspection
 Label verification
 Seal inspection
 Wrinkle alignment
 Carton inspection
 Automatic glue placement

10. Image processing : two sides
10
 Precision, objectivity, speed
 Saves tedious, inefficient manual inspection
tasks
 Must be sufficiently flexible and robust to cope
with biological flexibility
 Specialized hardware and software systems to
cope up with ever increasing demands of
speed and precision

11. Current scenario and Future
scope11
 Infancy stage
 Systems: successful under constrained
conditions for specific applications
 Most of the systems 2D and monochromatic
 Needs 3D systems with colour processing
 More robust, humanlike (artificial intelligence)
 Developing generic image processing systems
that can be used for multiple applications may
increase acceptability.

12. Conclusion
12
 Due to advances in electronics and computer
technologies, vision systems can be installed in
almost all food plants for an effective quality
evaluation/control operation.
 One of the biggest barriers in applying machine
vision to the food processing industry is the
system cost.
 Though the system cost for image processing
system has been significantly reduced the ratio of
cost to benefit is still unacceptable in many
potential applications.