Research of Key Technology for Microscopic Image Processing Platform in Sugar Cane Crystallization Process

• Main Authors: Y.M. Meng, K.Y. Zheng, W.X. Li, Y. Zhou, R.K. Qin, Z.H. Tang
• Format: Article
• Language: English
• Published: AIDIC Servizi S.r.l. 2015-12-01
• Series: Chemical Engineering Transactions
• Online Access: https://www.cetjournal.it/index.php/cet/article/view/4393

Realization of automatic sugar cane crystallization process depends on the development of advanced detective technology. At present, most factories still rely on manual operation to extract sucrose. And workers deduce the extent of further absorption and sucrose brix level by watching the seed size in mother liquor. Detective method based on visual observation of artificial morphology brings some disadvantages, such as strong subjectivity, observation error of each index, time-consuming, hard sledding and inefficient. Lack of detective method seriously hinders the development of automatic sugar crystallization process. As an on-line detection technology, image process has been widely used in industry. The use of image process helps computer to make real-time decision, thus raising automatic level. In order to meet the requirement of sucrose on-line real time testing during sugar crystallization process, this paper researches some key technology for microscopic image processing platform. Modular design approach is adopted in this platform, which includes hardware architecture and software architecture. Hardware architecture, which realizes the function of sucrose automatic sampling, image acquiring and transmission, consists of automatic sampling and imaging device, lighting source system and image acquisition system. Software architecture consists of the self-developed sugar particle image process software (SPIPS) based on VC++ 6.0, which realizes the function of image displaying and storage, image preprocess, image feature extraction and crystallization state prediction. Improved watershed algorithm is used to achieve the segmentation of the adhesive particles. The attribute reduction and state classification of the crystals is realized by combining rough set and Gaussian process classification method. Experimental result shows that this platform is able to meet the requirement of actual production. What’s more, this platform is faster and more precise than other detection methods.