A Study of the Geometric Characteristics and Diseases for Phalaenopsis Seedlings with Machine Vision

• Main Authors: Kuo-Yi Huang, 黃國益
• Other Authors: Tshen-Chan Lin
• Format: Others
• Language: zh-TW
• Published: 2002
• Online Access: http://ndltd.ncl.edu.tw/handle/97076172927863312849

博士 === 國立中興大學 === 農業機械工程學系 === 90 === Phalaenopsis is getting popular recently. In order to ensure competition in world market for Taiwanese floristic industry, an automatic production line is a key factor. In this study we used the image processing techniques to develop a sorting system for Phalaenopsis seedlings. The sorting system consists of the module of estimation of the geometric characteristics, the module of diseases detection, and the sorting machine. The scope of this research to develop the whole system includes: 1. Developing an algorithm for estimating characteristics of Phalaenopsis seedlings: The algorithm is developed to estimate the geometric characteristics of Phalaenopsis seedlings using the image processing techniques. 2. Developing an algorithm for detecting diseases of Phalaenopsis seedlings: The disease detection algorithm is established to detect bacterial soft rot (BSR), bacterial brown spot (BBS), and Phytophthora black rot (PBR) using image processing techniques. 3. Developing a sorting system: A sorting system for Phalaenopsis seedlings was designed and manufactured. The sorting system is composed of the sorting mechanism and control system. 4. Developing a software for the sorting system: The software includes: (1) image processing functions, (2) user guide, and (3) command reference. A methodology using machine vision to estimate the geometric characteristics of Phalaenopsis seedlings was established in this paper. The image processing techniques including the stem-center search method, the leaf-endpoint search method, the leaf number search method, the pot removing method, and the leaf-shape extraction procedure were applied to develop the algorithms that were used to estimate the geometric characteristics. Forty-four samples were investigated. Measurements taken manually and from estimation using our method were obtained and compared. The average relative errors between estimated values and measured results were 1.48% for the total number of leaves, 1.80% for the length, 2.44% and 3.90% for the span and the angle between two upper leaves, 4.02% for the width, and 7.04% for the length/width ratio. A novel system for detecting and classifying Phalaenopsis seedling diseases, including BSR, BBS, and PBR, was developed. The features of the lesion area of a Phalaenopsis seedling were extracted by Rayleigh transform and image processing techniques, such as hole-filling, erosion, dilation, opening, and closing operators. The detection line algorithm (DLA) was used to evaluate the lesion area. Five color features - Rmean, Gmean, Bmean, Gmax, and M were used in the classification procedure. A Bayes classifier was applied to classify BSR, BBS, and PBR of Phalaenopsis seedlings. One hundred and forty-four samples were used to evaluate the system. The methodology rapidly detected and classified these three Phalaenopsis seedlings diseases, at 1.78 sec/pot, to an accuracy of 88.2%. The disease detection capability of the system, without classifying the disease type, was as high as 96.5%. A sorting system for Phalaenopsis seedlings was designed and manufactured. This sorting system consists of four major parts: (1) image grabbing and positioning mechanism, (2) pattern recognition system, (3) display panel, and (4) control system. Four hundred and thirty pots of Phalaenopsis seedlings were used to test the sorting system. According to the results, we were able to achieve a rapid sorting of 21.15 sec/pot compared to 27.42 sec/pot by manual sorting, to an accuracy of 90.0% compared to 97.2% when sorting manually. Our machine can save up to 22.3% of the time used for manual sorting. The sorting system for Phalaenopsis seedling (SSPS) software 1.0 was developed. The SSPS 1.0 library had been established. The sub-programs and functions were described in the SSPS 1.0 reference manual.