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Method of intelligent agricultural pest image recognition based on machine vision algorithm

DISCOVER APPLIED SCIENCES 2024年第10期6卷 536页

作者： Han, Feifei Guan, Xiangbin Xu, Ming Shanghai Vocat Coll Agr & Forestry Dept Plant Sci & Technol Shanghai 201699 Peoples R China Henan Prov Plant Protect & Quarantine Stn Zhengzhou 450000 Henan Peoples R China Shanghai Lingang Fengxian Econ Dev Co Ltd Shanghai 200131 Peoples R China

In recent years, people have paid increasing attention to smart agriculture. Its core technology is to combine information technology such as computer vision with agricultural production. In agricultural production, the occurrence of pests and diseases can seriously affect agricultural production. Therefore, in agricultural production, the diagnosis and identification of agricultural pests and diseases is the key to improve crop yield. Traditional machine vision methods often rely on feature extraction and classifier design, lack of strong learning and generalization ability, and can not effectively control crop diseases and pests in a timely manner. In order to solve this problem, this paper designs an agricultural pest recognition system based on machine vision algorithm of fuzzy recognition theory, and applies it to agricultural pest recognition. Compared with the traditional machine vision method, this method has obvious advantages, and can effectively improve the monitoring and early warning ability of crop diseases and pests. The experimental results show that the highest recognition rate of the machine vision optimization algorithm based on fuzzy recognition theory is 98.06%, and the lowest recognition error rate is 5.83%. It can be seen that the machine vision recognition algorithm based on fuzzy recognition theory has good recognition effect and can be used in agricultural pest recognition system to help farmers carry out agricultural production and reduce economic losses caused by diseases and pests. The innovative integration of smart agriculture and artificial intelligence significantly improves the accuracy and real-time identification of crop pests and *** client-server image recognition system is developed to optimize crop pest monitoring *** recognition theory is applied to improve the recognition rate and classification effect of pest image features, and help precise control.

关键词： Intelligent agriculture Pest image recognition Intelligent recognition machine vision algorithm Rice leaf pests

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The Application of machine vision algorithm in Obstacle Recognition and Navigation of Logistics Robots 3

The Application of Machine Vision Algorithm in Obstacle Reco...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Liu, Lixun Zhou, Rongcheng Zhuhai College of Science and Technology Guangdong Zhuhai China

ISBN: (纸本)9798350317022

In various fields such as medical imaging, object detection, and video surveillance, multi view natural language query systems utilize image data to provide a more comprehensive perspective, allowing users to intuitively query and obtain information. Due to the lack of a deep understanding of natural language in the hard coded matching rule method, the query results do not match the user's intentions and are difficult to meet practical application needs. Therefore, this article introduces machine vision algorithms for optimization and improvement. Through two sets of simulation experiments, the following conclusion can be drawn: compared with traditional methods, the comprehensive average improvement of obstacle accuracy index is about 11.5%, while the comprehensive average improvement of navigation efficiency index is 13.35%. This study injects innovative momentum into the logistics industry, enabling logistics robots to quickly and accurately perceive and navigate obstacles in complex environments, thereby enhancing their intelligence level in tasks such as warehousing and sorting, and making substantial contributions to improving logistics efficiency and reducing costs. © 2023 IEEE.

关键词： Logistics Robot machine vision algorithm Navigation and Positioning Obstacle Recognition

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Design of mountain fire prevention monitoring system for transmission lines based on machine vision algorithms

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INTERNATIONAL JOURNAL OF EMERGING ELECTRIC POWER SYSTEMS 2023年第4期24卷 529-539页

作者： Dong, Shunhu Ding, Yuanjie Ma, Lishan He, Wei Lei, Xiaoping State Grid Guoluo Power Supply Co Guoluo 814000 Qinghai Peoples R China State Grid Qinghai Elect Power Co Informat Commun Transportat Inspect Ctr Informat Commun Co Xining 810001 Qinghai Peoples R China

With the continuous development of society, power grid has become a major guarantee for social and economic development, and its coverage is also expanding. The power grid not only covers some developed areas, but also extends to remote mountainous areas with complex changes in geological and climatic conditions. However, at the same time, line failures and power outages caused by mountain fires are becoming increasingly frequent, which poses a significant threat to the safety and stable operation of the power system. Therefore, how to solve the problem of mountain fire on transmission lines has become an important topic today. The main purpose of this study is to achieve effective monitoring of transmission line mountain fires and provide more assurance for the stable operation of the power system. This article discussed the transmission line and traditional mountain fire prevention monitoring system, and combined the current hot machine vision algorithm to design the overall mountain fire prevention monitoring system, focusing on analyzing the differences between this system and traditional systems. Through experiments, it was found that the monitoring accuracy of the mountain fire prevention monitoring system designed in this article has been improved by 10.6 % compared to the traditional system at location 4, 7.6 % at location 6, and 7.9 % at location 7. At the same time, the real-time performance and anti-interference performance of this system were also better than traditional systems, and the overall performance of the system was better. Practice has proved that the system can effectively overcome the interference of external environment, and monitor and identify the fire situation in real time and accurately, ensuring the safety of transmission lines. At the same time, the research on machine vision algorithms in this paper can also help promote the more perfect development of power systems. Practice has proved that this system can effectively overcome the i

关键词： machine vision algorithm monitoring system mountain fire prevention transmission line

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Study on R&D of Embedded Front-End Intelligent Monitoring System based on machine vision algorithm

Study on R&D of Embedded Front-End Intelligent Monitoring Sy...

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2nd International Conference on Material and Manufacturing Technology (ICMMT 2011)Progress Meeting of the Royal-Society-of-Tropical-Medicine-and-Hygiene's Annual Research

作者： Fu, Songyin Gong, Songchun Chen, Zheng Ningbo Univ Coll Informat Sci & Engn Ningbo 315211 Zhejiang Peoples R China

ISBN: (纸本)9783037852521

The project team has completed some research work in the earlier period. In this paper, the present mainstream intelligent network video monitoring system is compared and analyzed in details. Also, in accordance with the advantages and disadvantages of the multiple system architectures, a type of distributed system architecture is designed preliminarily. Thus, with regard to this architecture, the project team conducts a detailed design, segment many functional modules, puts forward specific design and implementation plans, and ultimately completes the construction of the model machine of the whole system.

关键词： machine vision algorithm Embedded Front-End Intelligent Monitoring System Research and Development Construction

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Extended target tracking filter with intermittent observations

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IET SIGNAL PROCESSING 2016年第6期10卷 592-602页

作者： Shi, Jie Li, Yinya Qi, Guoqing Sheng, Andong Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Jiangsu Peoples R China

This study addresses the problem of tracking extended target with intermittent observations. Based on practical applications, two Bernoulli distributed random variables are employed to describe the intermittent phenomenon of the positional measurements and the measurements of target extent, respectively. First, a machine vision algorithm is developed to solve the target shape parameters. Then, four sub-filters are designed according to the received observations and the achieved target shape parameters. The output of the proposed tracking filer can be obtained by the weighted-confidence fusion of the sub-filters. Finally, the machine vision algorithm is evaluated by the virtual target images created in OpenGL (Open Graphics Library) and the real images of a moving ship. The performance of the designed tracking filter is compared with the traditional tracking filter. The experiment results show the effectiveness of the machine vision approach;also the Monte-Carlo runs demonstrate that the provided tracking filter outperforms the traditional one with respect to accuracy.

关键词： target tracking tracking filters image filtering computer vision random processes image motion analysis Monte Carlo methods extended target tracking filter filter intermittent observations Bernoulli distributed random variables positional measurements target extent measurements machine vision algorithm target shape parameters sub-filter design weighted-confidence fusion virtual target images OpenGL Open Graphics Library moving ship images Monte Carlo method

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Multiple Drosophila Tracking and Posture Estimation algorithm 4

Multiple Drosophila Tracking and Posture Estimation Algorith...

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4th International Conference on Informatics, Electronics & vision (lCIEV)

作者： Arai, Shogo Sirigrivatanawong, Pudith Hashimoto, Koichi Tohoku Univ Grad Sch Informat Sci Sendai Miyagi Japan

ISBN: (纸本)9781467369022

The analysis of animal locomotion is critical for characterizing and ultimately understanding behaviour. While locomotion quantification of single animals is straightforward, simultaneous analysis of multiple animals in a group is challenging. If performed manually, such analyses are labour-intensive and potentially unreliable, thereby necessitating the use of machine vision algorithms for automatic processing. machine vision algorithms need to reliably label each animal and maintain all animal identities throughout the video-recorded experiment. This allows detailed characterization of behaviours such as taxis, locomotion and social interaction. In this study, we present an algorithm for analysing the locomotion behaviour of the fruit fly Drosophila melanogaster, a popular model organism in neurobiology. Our algorithm detects all flies inside a circular arena, determines their position and orientation and assigns fly identities between consecutive frame pairs. Position and orientation of the flies are accurately estimated with average errors of 0.108 +/- 0.006 mm (approximately 5 % of fly body length) and 2.2 +/- 0.2 degrees, respectively. Importantly, fly identity is correctly assigned in 99.5% of the cases. Our algorithm can be used to quantify the linear and angular velocities of walking flies in the presence or absence of various behaviourally important stimuli.

关键词： computer vision pose estimation Drosophila melanogaster animal locomotion animal locomotion quantification machine vision algorithm multiple Drosophila tracking neurobiology posture estimation algorithm algorithm design and analysis Animals Approximation algorithms Approximation methods machine vision Merging Neurons robot vision algorithm design and analysis neurobiology pose estimation Approximation method Approximation algorithms merger Drosophila melanogaster Flies Animal Locomotion Biomechanics images computer vision Animals personal identity

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A Research of Full-automatic Experiment Platform using machine vision for Water Quality Monitoring

A Research of Full-automatic Experiment Platform using Machi...

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International Conference on Mechanical Engineering and Instrumentation (ICMEI)

作者： Zheng, Hongyuan Zhang, Rong Hu, Yanqing Liu, Rong Beihang Univ Sch Mech Engn & Automat 37 Xueyuan Rd Beijing Peoples R China Beihang Univ Sch Energy & Engn Beijing Peoples R China

ISBN: (纸本)9783038350095

Japanese medaka (Oryzias latipes) is highly valuable in the field of monitoring the quality of drinking water. But as a result of the limitation of experiment platform and realization technology, the previous study cannot extract the characteristics which can reflect the toxicity of water real-time and accurately. This paper discusses a new experiment platform which can replace the fish tank, adjust the focal length of the camera and measure the swimming speed of medaka automatically. In order to achieve these functions, a precision positioning system using feedback technology, a pair of autofocus mechanism, a new observation method and a set of machine vision algorithm were adopted in this platform. The preliminary test results show that the designed platform and machine vision algorithm for extracting the characteristic information of medaka are effective and feasible. The platform can be widely applied to study some toxic substances' effects on medaka and to assess the degree of risk of the drinking water.

关键词： machine vision algorithm Water quality monitoring Automatic experiment platform Japanese medaka

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A Research of Full-automatic Experiment Platform using machine vision for Water Quality Monitoring

A Research of Full-automatic Experiment Platform using Machi...

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2013 International Conference on Mechanical Engineering and Instrumentation(ICMEI 2013)

作者： Hongyuan Zheng Rong Zhang Yanqing Hu Rong Liu School of Mechanical Engineering and Automation BeiH ang University School of Energy and Engineering BeiH ang University

Japanese medaka(Oryzias latipes) is highly valuable in the field of monitoring the quality of drinking water. But as a result of the limitation of experiment platform and realization technology, the previous study cannot extract the characteristics which can reflect the toxicity of water real-time and accurately. This paper discusses a new experiment platform which can replace the fish tank, adjust the focal length of the camera and measure the swimming speed of medaka automatically. In order to achieve these functions, a precision positioning system using feedback technology, a pair of autofocus mechanism, a new observation method and a set of machine vision algorithm were adopted in this platform. The preliminary test results show that the designed platform and machine vision algorithm for extracting the characteristic information of medaka are effective and feasible. The platform can be widely applied to study some toxic substances' effects on medaka and to assess the degree of risk of the drinking water.

关键词： machine vision algorithm Water quality monitoring Automatic experiment platform Japanese medaka

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Precision weed control system for cotton

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TRANSACTIONS OF THE ASAE 2002年第1期45卷 231-238页

作者： Lamm, RD Slaughter, DC Giles, DK Univ Calif Davis Dept Biol & Agr Engn Davis CA 95616 USA

A real-time robotic weed control system was developed and tested in commercial cotton fields. The precision weed control system was capable of distinguishing grass-like weeds from cotton plants and applying a chemical spray only to targeted weeds while traveling at a continuous speed of 0.45 m/s. The robot consisted of a real-time machine vision system, a controlled illumination chamber, and a precision chemical applicator In commercial cotton fields, the system correctly sprayed 88.8% of the weeds while correctly identifying and not spraying 78.7% of the cotton plants while traveling at 0.45 m/s.

关键词： robotics machine vision algorithm precision agriculture

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