About Book

Food engineering, as a branch of physical sciences, is used in food industry for food processing, building food machinery, ingredient manufacturing and instrumentation, etc. Food technology is an integral branch that ensures food safety and regulation through the application of technologies that standardize food manufacturing process. Food engineering includes, but is not limited to, the application of agricultural engineering, mechanical engineering and chemical engineering principles to food materials. The process of getting food from "farm to fork," as the saying goes, involves more than planting, harvesting, shipping, processing, packaging and distributing—though those are all key components. This book covers fundamental food principles in the design of food industry machinery. It presents a wide vision of food engineering, with an emphasis on topics related to machine vision identification, matlab GUI, machine milking, automatic colour analysis and machine learning methods. The book provides broad, yet technically detailed coverage of food engineering. It talks extensively about the different branches that fall under this category. The topics included in this book on food engineering are of utmost significance and are bound to provide incredible insights to the readers. It will serve as a valuable source of reference for those interested in this field and related branch.

Preface............................................................................................................v

Chapter 1 Rainfall Erosivity and Its Estimation: Conventional and Machine Learning Methods.......................................................................................1

Abstract...................................................................................................1

1. Introduction............................................................................................2

2. Computation of the R factor...............................................................3

3. Methods for the estimation of R........................................................6

4. Utilization of neural networks for the estimation of R................8

5. Conclusions..........................................................................................18

Conflict of interest..............................................................................19

Chapter 2 Machine Vision Systems – A Tool for Automatic Color Analysis in Agriculture.............................................................................................24

Abstract.................................................................................................24

1. Introduction..........................................................................................25

2. Machine vision systems.....................................................................27

3. Hardware and software requirements............................................31

4. Color analysis......................................................................................32

5. Applications of machine vision systems in agriculture..............34

6. Conclusions..........................................................................................45

Acknowledgements.............................................................................45

Chapter 3 Electric Machines: Tool in MATLAB...................................................52

Abstract.................................................................................................52

1. Introduction..........................................................................................52

2. Ferromagnetic core.............................................................................53

3. Single- and three-phase transformer...............................................58

4. DC machines........................................................................................66

5. Induction machines.............................................................................71

6. Conclusion............................................................................................75

Chapter 4 The Course of Machine Milking in Small Ruminants...................76

1. Introduction ........................................................................................76

2. Machine milking of small ruminants: based on the results of a

study by Olechnowicz (2006)...........................................................91

Chapter 5 Dynamic Simulation of Electrical Machines and Drive Systems

Using MATLAB GUI.............................................................................114

1. Introduction........................................................................................114

2. Design methodology for virtual models of electrical machines

and drives...........................................................................................115

3. Virtual models for analysis of dynamical properties of electrical

machines.............................................................................................116

4. Virtual models applied for synthesis of drive systems............136

5. Experiences with utilization of virtual models...........................140

6. Conclusion..........................................................................................141

Acknowledgement.............................................................................142

Chapter 6 Boosting Economic Growth Through Advanced Machine

Vision.........................................................................................................144

1. Introduction........................................................................................144

2. Machine vision in healthcare ........................................................146

3. Machine vision for sustainable growth........................................148

4. Machine vision in finance...............................................................150

5. Machine vision in innovation .......................................................154

6. CYBER II: A machine vision technology for various

application domains ........................................................................155

7. Conclusion..........................................................................................160

Chapter 7 Machine Vision to Determine Agricultural Crop Maturity.........164

1. Introduction........................................................................................164

2. Colour vision.....................................................................................165

3. Machine vision system.....................................................................165

4. Oil Palm FFB ripeness......................................................................166

5. Development of crop colour model..............................................168

6. Determination of oil content...........................................................170

7. Prediction of harvesting day..........................................................172

8. Real-time oil palm FFB maturity prediction model...................174

9. Conclusions........................................................................................176

Chapter 8 Machine Vision Measurement Technology and Agricultural

Applications.............................................................................................177

1. Introduction........................................................................................177

2. Acquiring images..............................................................................178

3. Commonly used image processing and analysis softwares.....185

4. Measurement types...........................................................................186

5. Agricultural application gallery.....................................................193

6. Future of machine vision measurement technology..................213

7. Acknowledgement.............................................................................213

Chapter 9 Machine Vision Identification of Plants...........................................216

1. Introduction........................................................................................216

2. Spatial variability of weed populations ......................................217

3. Machine vision...................................................................................218

4. Vegetation indices.............................................................................220

5. Computerized single leaf extraction.............................................225

6. Shape feature analysis......................................................................226

7. textural feature analysis...................................................................228

8. Plant species classification...............................................................229

9. Linking machine vision with weed management systems.......232

10. Conclusions........................................................................................233

Citation......................................................................................................247

Index...........................................................................................................249

About Author

Megan Halt received her Doctor of Philosophy (Ph.D.) in Food Science and Technology from Public Science University, Nova Scotia. Her primary areas of scholarly and research interests lie in the fields of food processing technologies and food microbiology. She serves as an Associate Lecturer at University College, Paraná and has also been presented the “Teacher of the Year” award. Halthas spent more than a decade in the food industry and has authored numerous articles and books, which have been published worldwide. She has attended numerous food science conferences and symposiums.