Application and Research of Computer Intelligent Technology in Modern Agricultural Machinery Equipment
Every country, including China, is deeply concerned and interested in the topic of agricultural machinery automation. The world’s population is growing at an astronomical rate, and as a result, the need of food is also growing at an astronomical rate. Farmers are forced to apply more toxic pesticide...
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| Vydané v: | Computational intelligence and neuroscience Ročník 2022; s. 1 - 11 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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United States
Hindawi
09.08.2022
John Wiley & Sons, Inc |
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| ISSN: | 1687-5265, 1687-5273, 1687-5273 |
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| Abstract | Every country, including China, is deeply concerned and interested in the topic of agricultural machinery automation. The world’s population is growing at an astronomical rate, and as a result, the need of food is also growing at an astronomical rate. Farmers are forced to apply more toxic pesticides since traditional methods are not up to the task of meeting the rising demand. This has a major impact on agricultural practices, and in the long run, the land becomes barren and unproductive. Intelligent technologies such as Internet of Things, wireless communication, and machine learning can help with crop disease and pesticide storage management, as well as water management and irrigation. In this paper, we design and analyze an intelligent system that automatically predicts the agricultural land features for irrigation purpose. Initially, the dataset is collected and preprocessed using normalization. The features are extracted using principal component analysis (PCA). For automatic prediction by the equipment, we propose heterogeneous fuzzy-based artificial neural network (HF-ANN) with genetic quantum spider monkey optimization (GQ-SMO) algorithm. Analyses and comparisons are made between the proposed approach and current methodologies. The findings indicate the effectiveness of the proposed system. |
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| AbstractList | Every country, including China, is deeply concerned and interested in the topic of agricultural machinery automation. The world’s population is growing at an astronomical rate, and as a result, the need of food is also growing at an astronomical rate. Farmers are forced to apply more toxic pesticides since traditional methods are not up to the task of meeting the rising demand. This has a major impact on agricultural practices, and in the long run, the land becomes barren and unproductive. Intelligent technologies such as Internet of Things, wireless communication, and machine learning can help with crop disease and pesticide storage management, as well as water management and irrigation. In this paper, we design and analyze an intelligent system that automatically predicts the agricultural land features for irrigation purpose. Initially, the dataset is collected and preprocessed using normalization. The features are extracted using principal component analysis (PCA). For automatic prediction by the equipment, we propose heterogeneous fuzzy-based artificial neural network (HF-ANN) with genetic quantum spider monkey optimization (GQ-SMO) algorithm. Analyses and comparisons are made between the proposed approach and current methodologies. The findings indicate the effectiveness of the proposed system. Every country, including China, is deeply concerned and interested in the topic of agricultural machinery automation. The world's population is growing at an astronomical rate, and as a result, the need of food is also growing at an astronomical rate. Farmers are forced to apply more toxic pesticides since traditional methods are not up to the task of meeting the rising demand. This has a major impact on agricultural practices, and in the long run, the land becomes barren and unproductive. Intelligent technologies such as Internet of Things, wireless communication, and machine learning can help with crop disease and pesticide storage management, as well as water management and irrigation. In this paper, we design and analyze an intelligent system that automatically predicts the agricultural land features for irrigation purpose. Initially, the dataset is collected and preprocessed using normalization. The features are extracted using principal component analysis (PCA). For automatic prediction by the equipment, we propose heterogeneous fuzzy-based artificial neural network (HF-ANN) with genetic quantum spider monkey optimization (GQ-SMO) algorithm. Analyses and comparisons are made between the proposed approach and current methodologies. The findings indicate the effectiveness of the proposed system.Every country, including China, is deeply concerned and interested in the topic of agricultural machinery automation. The world's population is growing at an astronomical rate, and as a result, the need of food is also growing at an astronomical rate. Farmers are forced to apply more toxic pesticides since traditional methods are not up to the task of meeting the rising demand. This has a major impact on agricultural practices, and in the long run, the land becomes barren and unproductive. Intelligent technologies such as Internet of Things, wireless communication, and machine learning can help with crop disease and pesticide storage management, as well as water management and irrigation. In this paper, we design and analyze an intelligent system that automatically predicts the agricultural land features for irrigation purpose. Initially, the dataset is collected and preprocessed using normalization. The features are extracted using principal component analysis (PCA). For automatic prediction by the equipment, we propose heterogeneous fuzzy-based artificial neural network (HF-ANN) with genetic quantum spider monkey optimization (GQ-SMO) algorithm. Analyses and comparisons are made between the proposed approach and current methodologies. The findings indicate the effectiveness of the proposed system. |
| Audience | Academic |
| Author | Liu, Jinqi Huang, Fan Zhou, Hongyu |
| AuthorAffiliation | 2 Hubei Packaging Equipment Engineering Technology Research Center, Wuhan 430068, China 1 Hubei University of Technology, Wuhan 430068, China |
| AuthorAffiliation_xml | – name: 1 Hubei University of Technology, Wuhan 430068, China – name: 2 Hubei Packaging Equipment Engineering Technology Research Center, Wuhan 430068, China |
| Author_xml | – sequence: 1 givenname: Hongyu surname: Zhou fullname: Zhou, Hongyu organization: Hubei University of TechnologyWuhan 430068Chinahbut.edu.cn – sequence: 2 givenname: Jinqi orcidid: 0000-0002-2395-9383 surname: Liu fullname: Liu, Jinqi organization: Hubei University of TechnologyWuhan 430068Chinahbut.edu.cn – sequence: 3 givenname: Fan orcidid: 0000-0003-2597-0612 surname: Huang fullname: Huang, Fan organization: Hubei University of TechnologyWuhan 430068Chinahbut.edu.cn |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35983150$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/j.jmapro.2020.09.018 10.1007/s42524-020-0107-3 10.1109/tii.2020.3035198 10.1016/j.compag.2018.03.012 10.1016/j.compeleceng.2021.107104 10.1177/20966083211052380 10.3390/agronomy10040573 10.1109/ACCESS.2020.3048415 10.3390/land10020213 10.52152/spr/2021.121 10.1108/caer-05-2017-0093 10.52152/spr.2020.01.104 10.1016/j.compag.2018.12.006 10.1016/j.aiia.2020.06.001 10.1016/j.scitotenv.2019.03.045 10.52152/spr.2020.100 10.3390/rs12071135 10.1016/j.inpa.2019.09.006 10.1016/j.jafr.2020.100033 10.3390/s18082674 10.1016/j.iot.2019.100142 |
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| Copyright | Copyright © 2022 Hongyu Zhou et al. COPYRIGHT 2022 John Wiley & Sons, Inc. Copyright © 2022 Hongyu Zhou et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 Copyright © 2022 Hongyu Zhou et al. 2022 |
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| References | 22 23 24 25 26 A. Sharma (9) 2020; 9 A. B. Orishev (11) 2020; 3040 10 12 14 15 16 19 V. K. Rajan (18) 2021; 8 R. N. Mody (2) 1995; 54 1 3 S. O. Salihu (17) 2022; 2 4 5 6 7 8 Z. Li (13) 2022 20 21 37538694 - Comput Intell Neurosci. 2023 Jul 26;2023:9849162 |
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| SubjectTerms | Agricultural equipment Agricultural equipment and supplies industry Agricultural land Agricultural practices Agricultural production Agriculture Agriculture - methods Algorithms Artificial intelligence Artificial neural networks Automation Barren lands China Computers Feature extraction Fuzzy logic Internet of Things Irrigation Machine learning Neural networks Optimization Pesticides Pesticides - analysis Plant diseases Principal components analysis Technology Technology application Water management Wireless communications |
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| Title | Application and Research of Computer Intelligent Technology in Modern Agricultural Machinery Equipment |
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