Feasibility of Online Diagnosis of Botrytis elliptica Disease in the Lilium Plant using the Machine Vision System and K-means

The article considers the issue of mathematical modeling of machine-building technologies of machine vision systems in the context of the transition to smart enterprises, which are the basis of modern Industry 4.0. It is noted that traditional approaches to modeling do not fully meet the requirements of digital production, which is characterized by high dynamism, the need for integration with cyber-physical systems, Internet of Things (IoT) technologies and adaptive control in real time. It is shown that modeling of machine-building technologies at a smart enterprise as a formalized approach is based on the specification of subsystems and a set of factors. The characteristics of mathematical models that can describe production processes at the enterprise with sufficient accuracy are given. In this case, the specifics of the structure, composition, key functional purposes and engineering, economic and its software and hardware aspects, as well as the conditions of machine-building production in accordance with the concept of a cyber-physical system, are taken into account. It is determined that according to the cyber-physical approach, the degree of automation of technological equipment varies from basic to high-level solutions. This requires modeling of production processes and technologies and the construction of mathematical models and their parameterization for further use in network interaction conditions when creating smart production and smart enterprises. It is found out that this requires the use of a machine (technical vision) system to ensure automation of product (service) quality control processes and management by processing and interpreting the information system. The possibilities of implementing a machine vision system at machine-building enterprises are identified. In this regard, such an artificial intelligence method as artificial neural networks is considered. The tasks in machine (technical) vision systems using the artificial neural network method are formulated. The advantages of this method in digital production and smart enterprises are determined. The integration of machine vision with robotic systems is clarified. A number of functions of the "machine vision-robot" complex and the possibilities of controlling the movement of robots during the task of controlling robotic platforms using a model of various types of artificial neural networks have been determined. The structure of an intelligent control system for robotic and mechatronic systems with a number of functional modules has been developed. An automated complex for the manufacture and assembly of parts and machines, intelligent production systems, have been considered at a smart enterprise. The functionality of intelligent production systems is based on modeling using mass service systems. Multiphase single- and multi-channel mass service systems are considered. The possibilities of models of automated production systems and the use of multi-level computer models in SCADA systems have been determined. The effectiveness of the created smart enterprises on the platform of a cyber-physical system and their integration into a single network of production systems of mechanical engineering have been shown. This involves the use of various automated design systems, technological preparation of production, a single database that will have a data management system about the manufacturer and mathematical modeling of advanced mechanical engineering technologies at smart enterprises.

Micro machine vision system is an important part of a micromanipulating system which has been used widely in many fields. As the research activities on the micromanipulating system go deeper, micro machine vision system catches more attention. In this paper, micro machine vision system is treated as a kind of machine vision system with constrains and characteristics introduced by specific application environment. Unlike the traditional machine vision system, a micro machine vision system usually does not aim at the reconstruction of the scene. It is introduced to obtain expected position information so that the manipulation can be accomplished accurately. The architecture of the micro machine vision system is proposed. The key issues related to a micro machine vision system such as system layout, optical imaging device and vision system calibration are discussed to explain the proposed architecture further. A task-oriented micro machine vision system for biological micromanipulating system is shown as an example, which is in compliance with the proposed architecture.

In recent years, much work has been reported on the development of microcomputer based machine vision systems. A substantial portion of this research assumes most of the items subjected for machine vision inspection, can be categorized by a regular geometrical shape. Most of the vision systems, and microcomputer-based vision systems in particular, are designed to perform singular tasks. They are niche oriented and designed to be used in an inflexible environment, and are designed to process regular objects. These systems are prohibitively expensive for small industrial concerns. The objective of the research was to develop and design a low cost, microcomputer based, machine vision system to process irregular objects. The specific irregular object application for this research is the grading operations for the tree seedlings at the Iowa Conservation Commission Nursery in Ames. The main emphasis of the project has been on achieving optimum processing speed, and functional versatility. Various phases required to process the tree seedling were formulated and developed. The system made an extensive use of highly efficient run-length coding techniques, featuring comprehensive line segment tracking with flexible segment discrimination characteristics. Specific areas of the image were subjected to a set of logical masks, in order to isolate, identify, and measure, various parameters of the seedling. The acquisition and processing time was fraction of a second. It was concluded that having a high resolution and noise tolerant image capture system was essential for processing irregular objects like tree seedlings.

This research focuses on the design and implementation of an intelligent machine vision and sorting system that can be used to sort objects in an industrial environment. Machine vision systems used for sorting are either geometry driven or are based on the textural components of an object’s image. The vision system proposed in this research is based on the textural analysis of pixel content and uses an artificial neural network to perform the recognition task. The neural network has been chosen over other methods such as fuzzy logic and support vector machines because of its relative simplicity. A Bluetooth communication link facilitates the communication between the main computer housing the intelligent recognition system and the remote robot control computer located in a plant environment. Digital images of the workpiece are first compressed before the feature vectors are extracted using principal component analysis. The compressed data containing the feature vectors is transmitted via the Bluetooth channel to the remote control computer for recognition by the neural network. The network performs the recognition function and transmits a control signal to the robot control computer which guides the robot arm to place the object in an allocated position. The performance of the proposed intelligent vision and sorting system is tested under different conditions and the most attractive aspect of the design is its simplicity. The ability of the system to remain relatively immune to noise, its capacity to generalize and its fault tolerance when faced with missing data made the neural network an attractive option over fuzzy logic and support vector machines.

The Purpose of this paper is threefold: (a) to summarize important parameters and procedures for verifying the measurement and recognition (classification) accuracy of machine vision algorithins/systems; (b) to alert the machine vision research community to the current, very inadequate practice in this important area; and (c) to propose some measures to improve this situation. Two example applications from our practice are given in order to illustrate experimental verification procedures we presented. The motivation for the paper is based on the fact that machine vision systems are very hard to model or simulate accurately, so realistic large scale experiments seem to be the only reliable means of assessing their accuracy. However, in the machine vision (research) community this veffication is seldomly done ade- quately. We feel that until this situation is improved, the transfer of research ideas into practice will be difficult.

A plant growth chamber equipped with a machine vision (MV) system was developed for the continuous, non-contact sampling and near-real-time evaluation of the top projected leaf area (TPLA) of lettuce (Lactuca sativa, cv. Ostinata) seedlings. A rotary table enabled automatic, individual presentation of the lettuce plants to the imaging system. Hourly measurements were continuously made for 16 plants from the first true leaf stage through 30 days from seeding. A near-infrared radiation source illuminated the plants during the dark period, permitting measurements without interrupting the 12 hour photoperiod. Daily minimum hourly change of TPLA for the plants occurred from 3 to 4 hours after the start of the light period. Most rapid increase in TPLA occurred from 4 to 5 hours after the onset of the dark period. The machine vision system was capable of determining a plant physiological response to the nutrient stress within 24 hours of the change of the nutrient regime.

The machine vision system is widely used in manufacturing factories. It is used to inspect the quality of products. This system reduces the inspection time and human error. Recently, there are various brand of machine vision systems. However, the machine vision systems are high-cost systems. There are two types of machine vision systems: smart cameras and PC bases. Generally, the cost of camera for smart camera system is higher than PC base. Especially, the high-resolution vision system is more expensive than the low-resolution. Therefore, this study proposes to develop the machine vision system from the smart phone. Since the smart phone is continuously improving, easy to reinstall (in the hardware damaged case), high resolution and lower cost than the machine vision system in the market. Thus, it is challenging to use the smart phone as an industrial machine vision system. In this study, the smart phone base machine vision system was used to detect the missing part application. The broken foil on the top of the milk carton was used to simulate the inspection process. The results found the smart phone's base machine vision system can detect the broken foil with an accuracy = 95% and an average processing time = 0.9 sec

Recent trends in developing computer and robotic vision systems are borrowing from research into biological vision systems. Reasoning for such methods stems from the fact that evolution has solved many of the problems that current artificial vision system designers are now facing.One such area that designers have borrowed from nature is that of attention. Attention is being employed successfully in many different forms to ease the cost of computation on input data received in machine vision systems; many times modeled after ideas borrowed from what we have learned about the visual attention system employed by humans.Another area in the human vision system that is being examined is the ability to build a symbolic representation of the visual-environment from scratch; such ability introduces a myriad of benefits and opens many new possibilities. All humans that exist are or were, at one time, babies that had no internal representation of line, color, or form. This was learned by observing, slowly, the world around us and later associated with linguistic representations to interrelate. Many now believe that any robust machine vision system will need to develop in a similar manner.Attention combined with learning can provide a powerful tool in creating machine vision systems. Past research has shown, however, that much of the learning that occurs in humans comes from unattended stimulus, referred to as implicit learning. Consequently, there is the possibility for large gains for machine vision systems that exploit implicit learning.The experiment presented hereafter aims to examine the importance of explicit learning as compared to implicit learning. In addition, it will look at how attention changes as users become conditioned to an environment, with the ultimate goal of applying what is learned towards the design of a machine vision system that not only builds its own representation of the visual-environment, but does so with data that is both attended and unattended.Participants are divided into two groups. Group 1 is used to measure the performance gains from implicit learning and conversely, Group 2 is used to measure the performance gains from explicit learning. Visual attention is recorded using the Eye-Link II eye tracker. Simply, it is a piece of head-gear fixed with cameras to pinpoint gaze-position. 3D gaze-position is determined using a neural network calibration program.The task is quite simple, and one that many researchers are familiar with. Each participant is placed in a sufficiently large maze that is displayed to them using an OpenGL program on an autostereoscopic monitor. The goal of the participant is to find the cheese that is hidden somewhere within the maze. The maze is quite similar in design to the mazes used to measure learning in rodents. However, the walls are textured using a light-red and dark-red brick pattern. The exact same maze is used for every trial in the experiment, but there are twenty versions of the task; ten for Group 1 and ten for Group 2. Group 1 participants are placed in the same location with ten different brick patterns; each brick pattern corresponds one-to-one to exactly ten different cheese-locations. Group 2 participants are placed in ten different locations using one brick pattern; each starting location corresponds one-to-one to exactly ten different cheese-locations. Each participant participates in two epochs a day for five consecutive days. An epoch consists of running through each of their ten versions of the maze presented in a pseudo-random order between epochs.Collected data will be in the form of measured running-time for each maze as well as gaze positions through the running of the maze collected using the Eye-Link II. At the end of the experiment, participants will be given a questionnaire to fill out. One of the questions will be aimed at determining any strategies employed to figure out the location of the cheese.Performance gains for participants in group 1 can be attributed to the implicit learning of the brick pattern to cheese-location association; assuming they did not figure it out and post it on the questionnaire. Whereas performance gains for participants in group 2 can be attributed to explicit learning. Due to the fact that it is the actual decisions made by participants that keyed the cheese-location, which must always be attended to.Aside from the goal of dividing benefits between implicit and explicit learning, the experiment aims to track differences in visual attention as participants become familiar with the tasks and environment. This also has possible important repercussions for an attended, learning machine vision system, by giving a new model for what exactly the system should be attending to.Very preliminary results suggest learning by both groups of participants, but it is too early to estimate to what extent learning occurs.

The success of machine vision systems in solving real-world problems will depend on how well they can balance the conflicting requirements of high accuracy, flexibility to operate under a wide range of environmental conditions, fast response time, and size constraints. Most machine vision systems developed in the past have sacrificed one or more of these factors in favor of the others. In this paper, we discuss our experience in developing high speed machine vision and world modeling systems for mobile robotics applications. A pipeline binocular stereo range detection system developed in our laboratory matches 256 x 256 pixel stereo image pairs in one second and generates 2- D and 3-D obstacle maps in near real-time. These obstacle maps then get integrated into pixel- and voxel-based dynamic world models. Using data provided by stereo cameras mounted on top of an indoor mobile robot, these systems have the capability to create very realistic models of the environment. An autonomous navigation system uses these environment models to successfully navigate a mobile robot in an indoor environment cluttered with dynamic and static obstacles.

This paper describes the machine vision system which has been developed for real-time measurement of industrially brushed yarns. This system provides a means for determining the brushing effect and for continuously introducing appropriate corrective signals to the yarn brushing machine. The system measures the brushed yarn during production, enabling the acquired data to be analysed and a fast response to be made, thus ensuring uniformity of quality. The four steps in the inspection of a brushed yarn involved (i) image acquisition; (ii) image enhancement; (iii) feature extraction; and (iv) image analysis and interpretation are reported.

Automated sorting of sweetpotatoes is necessary to reduce labor dependence and costs that are significant at today’s sweetpotato packing sheds. Although optical sorters have been widely adopted in commercial packing lines for many horticultural commodities, there remains an unmet need to develop dedicated technology for the automated grading and sorting of sweetpotatoes. Sorting mechanisms are the critical component that physically segregates products according to quality grades determined by a machine vision or imaging system. This study presents the new engineering prototypes and evaluation of three different pneumatically powered mechanisms for sorting sweetpotatoes online. Among the three sorters, the sorting mechanism, which employs a linear air cylinder to drive a paddle directly striking products, achieved the best overall accuracy and repeatability of 98% and 96.8%, respectively, at conveyor speeds of 4–12 cm/s. The sorter based on a rotary actuator also delivered decent accuracy and repeatability of 97.9% and 95.6%, respectively. The best-performing sorting mechanism was integrated with a machine vision system that graded sweetpotatoes based on size and surface defect conditions to separate graded sweetpotatoes into three quality categories. The errors of 0–1% due to the sorting process were obtained at conveyor speeds of 4–12 cm/s, confirming the efficacy of the manufactured sorting mechanisms. There was a declining trend with the conveyor speed in the performance of the sorting mechanisms when evaluated either in a standalone or integrated configuration. The proposed sorting mechanisms that are simple in construction and operation and of low cost are useful for developing a more full-fledged sorting system. More research is needed to enhance sorting performance and conduct extensive tests at higher conveyor speeds for practical application.

Machine vision systems play a vital role in entirely automating the evaluation of surface roughness due to the hitches in the conformist system. Machine vision systems significantly abridged the ideal time and human errors for evaluation of the surface roughness in a nondestructive way. In this work, face milling operations are performed on aluminum and a total of 60 diverse cutting experiments are conducted. Surface images of machined components are captured for the development of machine vision systems. Images captured are processed for texture features namely RGB (Red Green Blue), GLCM (Grey Level Co-occurrence Matrix) and an advanced wavelet known as curvelet transforms. Curvelet transforms are developed to study the curved textured lines present in the captured images and this module is capable to unite the discontinuous curved lines present in images. The CNC machined components consists of visible lay patterns in the curved form, so this novel machine vision technique is developed to identify the texture well over the other two extensively researched methods. Artificial Neural Network-Particle Swarm Optimization (ANN-PSO) intelligent models are developed to evaluate the surface roughness from texture features. The model average error attained using RGB, GLCM, Curvelet transform-based machine vision systems are 12.68, 7.8 and 3.57 respectively. In comparison, the results proved that computer vision system based on curvelet transforms outperformed the other two existing systems. This curvelet based machine vision system can be used for the evaluation of surface roughness. Here, image processing might be crucial in identifying certain information. One crucial issue is that, even as performance improves, cameras continue to get smaller and more affordable. The possibility for new applications in Industry 4.0 is made possible by this technological advancement and the promise of ever-expanding networking.

BACKGROUND: Minolta and machine vision are two different instrumental techniques used for measuring the colour of muscle food products. Between these two techniques, machine vision has many advantages, such as its ability to determine L*, a*, b* values for each pixel of a sample's image and to analyse the entire surface of a food regardless of surface uniformity and colour variation. The objective of this study was to measure the colour of irradiated Atlantic salmon fillets using a hand‐held Minolta colorimeter and a machine vision system and to compare their performance.RESULTS: The L*, a*, b* values of Atlantic salmon fillets subjected to different electron beam doses (0, 1, 1.5, 2 and 3 kGy) were measured using a Minolta CR‐200 Chroma Meter and a machine vision system. For both Minolta and machine vision the L* value increased and the a* and b* values decreased with increasing irradiation dose. However, the machine vision system showed significantly higher readings for L*, a*, b* values than the Minolta colorimeter. Because of this difference, colours that were actually measured by the two instruments were illustrated for visual comparison. Minolta readings resulted in a purplish colour based on average L*, a*, b* values, while machine vision readings resulted in an orange colour, which was expected for Atlantic salmon fillets.CONCLUSION: The Minolta colorimeter and the machine vision system were very close in reading the standard red plate with known L*, a*, b* values. Hence some caution is recommended in reporting colour values measured by Minolta, even when the ‘reference’ tiles are measured correctly. The reason for this discrepancy in colour readings for salmon is not known and needs further investigation. Copyright © 2009 Society of Chemical Industry

This paper presents a system for automated, non-contact, and flexible prediction of surface roughness of end-milled parts through a machine vision system which is integrated with an artificial neural network (ANN). The images of milled surface grabbed by the machine vision system could be extracted using the algorithm developed in this work, in the spatial frequency domain using a two-dimensional Fourier transform to get the features of image texture (major peak frequency F 1, principal component magnitude squared value F 2, and the average gray level G a). Since F1 is the distance between the major peak and the origin, it is a robust measure to overcome the effect of lighting of the environment. The periodically occurring features such as feed marks and tool marks present in the gray-level image can be easily observed from the principal component magnitude squared value F 2. The experimental machining variables speed S, feedrate F, depth of cut D, and the response extracted image variables F 1, F 2, and G a could be used as input data, and the response surface roughness R a measured by Surfcorder SE-1100 (traditional stylus method) could be used as output data of an ANN ability to construct the relationships between input and output variables. The ANN was trained using the back-propagation algorithm developed in this work due to its superior strength in pattern recognition and reasonable speed. Using the trained ANN, the experimental result had shown that the surface roughness of milled parts predicted by machine vision system over a wide range of machining conditions could be got with a reasonable accuracy compared with those measured by traditional stylus method. Compared with the stylus method, the constructed machine vision system is a useful method for prediction of the surface roughness faster, with a lower price, and lower environment noise in manufacturing process. Experimental results have shown that the proposed machine vision system can be implemented for automated prediction of surface roughness with accuracy of 97.53%. The results are encouraging that machine vision system can be extended to many real-time industrial prediction applications.

A pneumatic seed meter equipped with a machine vision system and a mechatronic drive was developed to reduce the incidence of missed seeding during mechanical corn planting. The machine vision system monitored the seed plate and checked if seeds did not attach to the seed plate holes. The rotation speed to the seed plate was adjusted by the mechatronic drive based on the presence and absence of seeds attached to the seed plate holes. The reliability of the mechatronic drive in regulating the seeding rate, and the accuracy of the machine vision system in identifying filled and unfilled seed plate holes were both tested. The combined system of the machine vision system and the mechatronic drive was also tested if it can effectively reduce the incidence of missed seeding in a stationary laboratory rig under simulated speed settings of 2kph, 4kph, and 6kph. It was found out that the drive did not maintain a consistent seed delivery rate, which can be attributed to the limited processing capacity of the microcontroller used to control the drive. The machine vision system was highly accurate as it only committed a 0.2564 % error out of 780 images. The machine vision system of the seed meter, at 2kph and 4kph simulated speed settings, committed significantly, at 95% level of confidence, less missed seeding in the conducted treatments in comparison to the setup where the seed meter was operated without the machine vision system. The treatments wherein the machine vision system was used, however, committed more missed seeding at 6kph simulated speed setting.