This document discusses using artificial neural networks to grade broccoli based on color and shape parameters measured through image processing. There are two stages: image processing to analyze color (using RGB, L*a*b*, and hue angle) and shape (area and roundedness); and training neural networks on these parameters to classify broccoli grades. Testing showed a radial basis function network achieved high accuracy with low error rates compared to a feed-forward backpropagation network. Neural networks show potential for accurate broccoli grading.

1. Broccoli Grading Using Artificial Neural Networks TRISHA SINGH B070309EE

2. Anti-Cancer Hope The green florets used as the edible part of broccoli (Brassicaoleracea) with the tender texture and flavor, are high in antioxidants, but more importantly, in anti-cancer compounds. That makes broccoli an important vegetable for exportation. Introduction

3. Introduction Low Shelf-Life The quality decay of broccoli is fast during post-harvest storage. Yellowness appears within 2~3 days at room temperature (about 5°C). Weight loss and yellowness are the main obstacles for fresh keeping in the post-harvest.

4. Introduction Grading Broccoli The artificial grading standards of broccoli mainly include the weight, colorand external quality. It has been discovered that there is a good relationship between instrumental color, sensory yellowness and chlorophyll content in cooked broccoli florets, and the chlorophyll content is a good index for evaluating the quality decay of broccoli florets during storage. With the availability of technologies such as neural networks, it is possible to grade broccoli with machine vision.

5. Method There are two stages involved in the grading of broccoli Analysis through Image Processing Grading using Artificial Neural Networks Introduction

6. Other applications Neural networks combined with image processing are being used to classify eggs, grade apples and other vegetables and even to classify animals. Introduction

7. Image Processing The first step in the process of grading broccoli is to measure the color of the broccoli using a computer vision system. Then, two kinds of analyses are performed: Color Analysis Shape Analysis Image Processing

8. Image Processing Color Analysis Colorwas first captured in RGB (Red, Green, Blue) system and then determined by reflectance mode and expressed by L*(luminosity), a* (green-red) and b* (blue-yellow) parameters. Broccoli surface color can be expressed as H° (Hue angle, arctan(b*/a*)), and the color of the florets surface can be indicated by TCD (Total Color Difference).

9. Image Processing Shape Analysis Using the image processing software, the area S and length L of the broccoli were determined. Then, the value of roundedness of the broccoli was calculated as E is between 0 and 1, and the higher value of E indicated better quality.

10. Image Processing Flow Chart of Image Processing and Analysis

11. Grading using ANN Grading using ANN Different artificial neural networks can be used to grade the broccoli on the basis of the following five parameters: b – corresponding to yellowness of the broccoli TCD – Total Color Difference H° - Hue Angle E - Roundedness S – Yellowness area Here, nntool in MATLAB is being used to perform the training and simulations.

12. Grading using ANN Grading thresholds of broccoli color and shape parameters

13. Grading using ANN Training Sets Used

14. Results Using feed-forward model withback-propagation algorithm A feed-forward back-propagation network using 10 neurons was trained and simulated using the 14 training sets shown in the previous slide. The following results were obtained: While training: Output: [1 1 1 1 1 1 1 1 1 4] Error: [0 0 0 1 1 2 2 2 2 6.175e-006] While simulation: Output:[1 1 1 1 1 1 1 1 1 1 1 1 4 4] Error: [0 0 0 1 1 1 1 2 2 2 2 2 5.0759e-006 6.175e-006] The errors are not negligible.

15. Results Using feed-forward model withback-propagation algorithm

16. Results Using feed-forward model withback-propagation algorithm Output: Error:

17. Results Using Radial Basis Function The following results were obtained: Using RBF (fewer neurons) Output: [1 1 1 2 2 2 2 3 3 3 3 3 4 4] Error: [8.8818e-016 1.1102e-015 9.992e-016 1.3323e-015 8.8818e-016 8.8818e-016 1.5543e-015 8.8818e-016 8.8818e-016 1.3323e-015 8.8818e-016 8.8818e-016 0 8.8818e-016] Using RBF (exact fit) Output: [1 1 1 2 2 2 2 3 3 3 3 3 4 4] Error: [1.1102e-016 1.1102e-016 3.3307e-016 2.2204e-016 2.2204e-016 4.4409e-016 4.4409e-016 0 0 0 4.4409e-016 0 0 0]

18. Conclusion The RBF (exact fit) has a lesser error than RBF (fewer neurons). The RBF (fewer neurons) however has a very low error, much lesser than feed-forward back propagation network and is quiet accurate in itself. So this network can be used for Broccoli grading with a pretty good accuracy. Greater accuracy can be achieved by using larger number of training sets. Results

19. References IEEE ICMA 2007 Paper Kang Tu, KeRen, Leiqing Pan and HongwenLi, “A Study of Broccoli Grading System Based on Machine Vision and Neural Networks”, Proceedings of the 2007 IEEE International Conference on Mechatronics and Automation August 5 - 8, 2007, Harbin, China, pp.2332 - 2336, 2007.