Bio-Sensing Engineering (Advanced Course)
| Numbering Code | Year/Term | 2022 ・ Second semester | |
|---|---|---|---|
| Number of Credits | 2 | Course Type | Lecture |
| Target Year | Target Student | ||
| Language | Japanese and English | Day/Period | Tue.3 |
| Instructor name | KONDOU NAOSHI (Graduate School of Agriculture Professor) | ||
| Outline and Purpose of the Course | Machine vision technologies have become important and common in agricultural production because recent cameras are so small and cheap that they can be usable in any machines and devices. Since images give us many kinds of information and are suitable to measure uncertain shape and various colors, the technologies are already used in agricultural facilities and field machines. Purpose of lecture is to learn higher techniques of softwares to extract features of agricultural products or foods on images as well as the fundamentals of machine vision system components for applying to agricultural production the infromation extracted from images acquired in fluctuated conditions. In addition, other types of sensing systems, for example ultrasonic sensor, photo interrupter, laser sensor, encoder, potentiaometer and others used as external and internal sensors for machines, are also introduced. | ||
| Course Goals |
1. Students can understand how to construct machine vision systems to measure uncertain property-agricultural products in fluctuated conditions. 2. Students can learn make algorithms to extract features which students would like to measure using image processing. 3. Students can discuss on what kinds of sensing systems are necessary in a given condition or object. |
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| Schedule and Contents |
Below topics will be lectured. 1. Basic components of machine vision systems to measure agricultural products 2. Design of machine vision system 3. Calibration of machine vision 4. Type of lamp for biological objects and lighting method 5. Spectral reflectance of agricultural properties 6. Preprocessing methods for image analysis 7. Image recognition based on physical properties and plant growth rule 8. Convolution filters 9. How to extract features from images aquired in the fields 10. Binary image and multilevel image 11. Pattern recognition 12. Hough transform 13. Fuzzy control and neural networks 14. Sensing systems of automatic machines |
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| Evaluation Methods and Policy | Tests in class and reports are evaluated. Refer to '2017 Guide to Degree Programs' for attainment levels of evaluation | ||
| Course Requirements | It is desirable to take undergraduate courses "Measument Science","Phisical and Biological Properties of Agricultural Products," and graduate course "Seminar I on Bio-Sensing Engineering." | ||
| Study outside of Class (preparation and review) | Assignments and homeworks should be submitted by the next class. Read fundamental image processing books. | ||
| References, etc. |
Robotics for bioproduction systems, Kondo, N. and Ting, K.C. Applied Image Processing, McGrawHill, Gonzalez, ASABE, Awcock, G.W. and Thomas, R. Digital Image Processing, Addison Wesley, R.C.and Woods, R.E |
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