The thesis develops an image recognition system that can be installed on the garlic sorting machine to check whether the garlic is damaged. The farmer can process the garlic more accurately and quickly. The garlic machine can reduce the manpower while classifying the garlic. The system utilizes software and hardware design techniques to develop a garlic sorter with a multi-layer perceptron neural network. Firstly, the system uses a personal computer to perform a Multi-Layer Perceptron Neural Network (MLP-NN) training kit written by OpenCV software. The training kit uses self-photographed sample images to generate Gray-Level Co-Occurrence Matrix (GLCM) by a statistical method. Then the GLCM calculates the characteristic values such as entropy and contrast. These values are inputted into the MLP-NN function for training. After that, the weight values generated from training are realized to a Field Programming Gate Array (FPGA) chip. The MLP-NN function is realized with FPGA chip for accelerating the garlic sorting process.
In order to achieve low-latency and high-speed system performance, the MLP-NN is realized by a XC7A100T-1CSG324C chip on Digilent Nexys 4 DDR FPGA development board. It uses only 2510 Flip Flops, 6054-bit Lookup Table, and 208 DSP48E of the FPGA chip. The system can achieve 93.87% accuracy with hardwired MLP-NN function.
[1]K. Li, Y. Yang, K. Liu, S. GU, Q. Zhang, L. Zhao, “Determinationand grading of Anthurium based on machine vision,” Transactions of the Chinese Society of Agricultural Engineering, Vol. 29, N0. 24, pp. 196-203, Dec. 2012.
[2]A. A. Masoumi, A. Rajabipoor, L. G. Tabil and A. A. Akram, “Physical Attributes of Garlic (Allium sativum L.),” Journal of Agricultural Science and Technology, Vol. 8, pp. 15-23, 2006.
[3]N. Zhang, “The Garlic Classification Based on Linear Regression Model,” Proceedings of 2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer, pp. 1148-1151, Dec. 2013, China.
[4]C. Gao and H. Zhang, “Study on the Direction Identification and Alignment of Garlic Scaly Bud,” Advanced Materials Research, Vol. 482-484, pp. 220-223, 2012.
[5]C. Gao and H. Gao, “Direction Identification System of Garlic Clove Based on Machine Vision,” TELKOMNIKA, Vol. 11, No. 5, pp. 2323-2329, May 2013.
[6]L. Zhao and W. Song, “Research of Grading System of Real-time Detection for Head of Garlic Based on Machine Vision,” Proceedings of 2015 International Industrial Informatics and Computer Engineering Conference, pp. 1884-1888, Mar. 2015.
[7]Peeling Garlic Color Sorter /Pinenut Kernel sorting machine, Baiteguangdian,https://www.alibaba.com/product-detail/Peeling-Garlic-Color-Sorter-Pinenut-Kernel_60493077274.html?spm=a2700.7724857.main07.38.1f9de533UtNgYd&s=p. Retrieved Dec. 2017.
[8]Peanut Color Sorting Machine, Promech Industries Pvt. Ltd.,http://www.sortermachine.com/peanut-color-sorting-machine-2365277.html. Retrieved Dec. 2017.
[9]HELIUSTM & HELIUSTM P SORTING MACHINES: OVERVIEW, TOMRA systemsASA,https://www.tomra.com/en/sorting/food/sorting-equipment/helius/. Retrieved Dec. 2017.
[10]Automatic Peeled Garlic Clove Vibrate Sorting Machine, ROMITER GROUP,http://www.garlicprocess.com/automatic-peeled-garlic-clove-vibrate-sorting-machine/. Retrieved Dec. 2017.
[11]R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classitication,” IEEE Trans. on Syst. Man Cyber, Vol. 3, No. 6, pp.610-621, 1973.
[12]L. K. Soh, and, C. Tsatsoulis, “Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices”, IEEE Trans. on Geosci. Remote Sens, Vol. 37, No. 2, pp.780-794, 1999.
[13]A. Baraldi, and F. Parmiggiani, “An investigation of the textural characteristics associated with gray level co-occurrence matrix statistical parameters,” IEEE Trans. on Geosci. Remote Sens, Vol. 33, No. 2, pp.293-303, March 1995.
[14]J. Hertz, A. Krogh & R. G. Palmer, Introduction to the Theory of Neural Computation, pp.13, Addison-Wesley. 1991.