# Collection of Group Characteristics of Pleurotus Eryngii Using Machine Vision

Yunsheng Wang, Changzhao Wan, Juan Yang, Jianlin Chen, Tao Yuan, Jingyin Zhao

► To cite this version:

Yunsheng Wang, Changzhao Wan, Juan Yang, Jianlin Chen, Tao Yuan, et al. Collection of Group Characteristics of Pleurotus Eryngii Using Machine Vision. Third IFIP TC 12 International Conference on Computer and Computing Technologies in Agriculture III (CCTA), Oct 2009, Beijing, China. pp.98-103, 10.1007/978-3-642-12220-0_15. hal-01061720

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<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
COLLECTION OF GROUP CHARACTERISTICS
OF PLEUROTUS ERYNGII USING MACHINE
VISION

Yunsheng Wang<fnref n="1" />, Changzhao Wan<fnref n="1" />, Juan Yang<fnref n="1" />, Jianlin Chen<fnref n="1" />, Tao Yuan
<fnref n="1" />, Jingyin Zhao 1,.*

**Abstract:** An information collection system which was used to group characteristics of pleurotus eryngii was introduced. The group characteristics of pleurotus eryngii were quantified using machine vision in order to inspect and control the pleurotus eryngii house environment by an automated system. Its main contents include the following: collection of pleurotus eryngii image; image processing and pattern recognition. Finally, by analysing pleurotus eryngii image, the systems for group characteristics of pleurotus eryngii are proved to be greatly effective.

**Keywords:** Pleurotus eryngii, Group characteristics, Machine vision, Collection

# 1. INTRODUCTION

World's edible fungi output reached 13 million tons at 2006, China is the largest producer of edible fungi, edible fungi production in China is the world's edible fungi production for more than 70%. Agricultural production in China, the sixth production of edible fungi, edible fungi have become the pillar industries in the agricultural economy (Huang Chienchun, 2006; Lu Min, 2006).

At present, the small-scale, extensive management production of edible fungi in China is still the main form, although this mode of production and low cost initial investment, but subject to natural risks and market risks is

<footnote n="1">¹ Technology & Engineering Research Center for Digital Agriculture, Shanghai Academy of Agriculture Sciences, Shanghai, P. R. China 201106</footnote>

<footnote>* Corresponding author, Address: No.2901 Beidi Road, Shanghai, 201106, P. R. China, Tel: +86-21-62204989, Email: wys188@163.com</footnote>
very weak, it is difficult to guarantee product quality, production efficiency
is very low. Therefore, the industrialization and standardization of edible
fungi production has become the development direction of edible fungi
industry.

In the early 1990s, China has introduced several edible fungi production
line, so that productivity increased a hundredfold, after nearly 20 years of
practice and exploration, China has gradually developed a model of
development of mushroom industry.

Edible fungi production in factories, we find that the edible fungi
cultivation of industrial technology as the core, "Mushroom house climate
control system" continues to experience-based control, far from being done
in accordance with the edible fungi production automation features of the
Tracking control. Edible Fungi groups uniformity and the uniform shape and
other characteristics of indicators, industrial cultivation of edible fungi has
been the main basis for control measures. For the current status of these
indicators of edible fungi in the diagnosis of a major in mind the number of
measurements and visual way, there is a slow investigation, the charges,
subjective strong, and the error of defects, and some features were also
difficult to describe in considerable Restrictions on the extent of the
industrial production of edible fungi in time during the strain of scientific
management, industrial production of edible fungi is present on the
"bottleneck" problem. Therefore, exploration accurate and rapid collection of
edible fungi groups feature of the new methods, carefully observe the growth
of edible fungi minor changes, summed up the seasons, days and nights
change on the growth of edible mushrooms, edible fungi to establish the best
model of environmental control So that the quality of edible mushroom
cultivation factory stability, cultivation and management of fully automated
intelligent, and machine vision technology in the industrial production of
edible fungi in the show great potential for application.

Machine vision research using computers to simulate the visual function
of things from an objective image to extract information, be understood to be
processed and eventually used for the actual detection, measurement and
control. Machine vision technologies, including image acquisition, image
processing and pattern recognition, machine vision can be simulated by the
human eye to the edible mushrooms to the close-up photography visible
spectrum, and then use artificial intelligence, digital image processing
techniques to analyze the image information, access to research Object
required information.
## 2. RELATED RESEARCH

The use of machine vision technology feature extraction Mushroom has undertaken a number of studies, the representativeness of the research results:

(Van De Voo ren et al., 1992) The use of machine vision technology to determine morphological characteristics of a variety of mushrooms, Circularity, Bending Energy, Sphericity and Eccentricity and so on to describe the shape of the characteristics of mushrooms; (Yu GaoHong et al., 2005) Identification of the use of machine vision algorithms for contour description of the individual mushroom; (Vízhányó et al., 1998) color image analysis of edible fungi of the mechanical damage in the color and different color sick to maximize the commercial value; (Tünde, et al., 2000) The use of image enhancement algorithm to distinguish between diseases of the color difference between edible fungi; (Heinemann et al., 1994) The use of machine vision technology for classification of edible fungi; (NJ Kusabs et al., 2006), such as the use of machinery vision technology from a number of morphological parameters for grading of edible fungi; (Van Loon et al., 1996) The use of machine vision to detect the mushroom the size of individual developmental stages; (Tillett et al., 1989) in the edible mushroom harvesting machines use machine vision to locate Mushroom; (Tillett et al., 1991) of the edible fungi mushroom bed positioning algorithm; (Reed et al., 1995) The use of machine vision to identify the mushroom mushroom harvest size.

Study on the application of machine vision are mostly limited to simple, idealized monomer, the complex problem of overlapping research groups small, and the growth of the state of the monomer can not accurately reflect the actual population growth of the law can not be used to guide production management practices. Characteristics of the identifiable group because of its image with a lot of interference factors for image segmentation and feature extraction very difficult, very difficult to identify, and thus need to study more efficient segmentation algorithm based on feature extraction, as the demand for the development of applications depth, machine vision technology will be gradually applied to the recognition of group identities.

## 3. SYSTEM INTRODUCTION

### 3.1 System Design

Precise control through the small environment the production of pilot
experiments with the factory-based, as the leading machine vision
technology, neural networks, machine learning as the link for example,
integrated expert system and a variety of mushroom production technology
and methods, and make full use of mushroom production expertise to
achieve the characteristics of the smart machine groups mushroom
identification and diagnosis of mushroom growth of the state of the
technological breakthrough.

The overall technology map as shown in Fig.1

<figure><img src="image_2.png" /><figcaption>Fig.: Overall technology map.</figcaption></figure>

3.2 Research methods and the implementation of the
program

The implementation of the program in Fig.2

(1) Study the characteristics of pleurotus eryngii, the establishment of a wealth of examples of environmental test samples of small rooms, as a group of image characteristics and growth of information, data access to sources of information. Controlled trial of small environment, including different temperature, humidity, CO2 concentration to deal with. Industrial production of samples, including groups of as many as possible samples of the edible fungi. Through a rich mushroom habitat type and sample number of learning samples, to improve the accuracy of the machine to identify and enhance the applicability of the future to build systems.

(2) Pleurotus eryngii groups to carry out multi-dimensional space-time image data acquisition. pleurotus eryngii with cameras in key growth period, from the top, middle and base in different parts of using down, up, at eye level groups depending on the perspective of image; used traditional method
homogeneous simultaneous determination of edible fungi and pleurotus eryngii covered neatly color, strength and other characteristics of growth examples of learning data, and invited experts in edible fungi pleurotus eryngii growth of the state assessment; the establishment of pleurotus eryngii growth of the state information database.

(3) Image feature extraction, image feature database to establish. To obtain images of pleurotus eryngii pretreatment to remove noise; to degradation caused by the phenomenon of recovery. The use of morphological operators and Hough transform techniques for the separation characteristics. Pleurotus eryngii growing in view of different culture production is based on different indicators, the growth of edible fungi in the group stage of the growth characteristics and state indicators to be classified by level of importance to determine the main objectives. Comprehensive application of image recognition, neural networks, machine learning from examples, such as fuzzy mathematical algorithm to be addressed, and through comparison, the screening, to determine the optimal algorithm. Difficult for some of the characteristics of image recognition, will take full advantage of the experience of mushroom experts, in order to relax the requirements of machine vision technology.

(4) Summary of mushroom factory production management knowledge and experience to create the rules, the establishment of image recognition applications knowledge.

(5) Characteristics of groups in the pleurotus eryngii image acquisition, intelligent identification on the basis of the establishment of sets of machine vision technology in pleurotus eryngii growth of the state of diagnosis index system and technical specifications.
<figure><img src="image_3.png" /><figcaption>Fig.2 Groups the characteristics of acquisition and pleurotus eryngii growth of the state of rapid diagnostic technology map.</figcaption></figure>

## 4. SYSTEM CHARACTERISTICS AND PROSPECTS

System is to be used in machine vision technology to identify intelligence mushroom groups and growth characteristics of the state of the practice of rapid diagnosis of an attempt to change the traditional mushroom factory groups characterized by the production of visual and manual collection method to enable automatic environmental control mushroom house intelligent.

Research based on the factory production process of edible fungi on mushroom growing demand for dynamic monitoring, application prospects, raise the level of mushroom factory production management.
## ACKNOWLEDGEMENTS

This study was supported by National Key Technology R&D Program (NO.2006BAD10A11), Shanghai Municipal Science and Technology Commission Project (073919103) and Shanghai Municipal Science and Technology Commission project (08DZ2210600).

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