176
Views
4
CrossRef citations to date
0
Altmetric
Computers & Computing

An Ontology-based Knowledge Mining Model for Effective Exploitation of Agro Information

&

References

  • M. A. d. A. Leite and I. L. M. Ricarte, “Fuzzy information retrieval model based on multiple related ontologies,” in 2008 20th IEEE International Conference on Tools with Artificial Intelligence, 2008, pp. 309–16, DOI:10.1109/ICTAI.2008.72.
  • J. Lai, Y. Wang, R. Zhang, X. Gu, T. Yu, and J. Li, “Aggregating multiple ontology similarity based on IOWA operator,” in 2010 2nd International Workshop on Database Technology and Applications, 2010, pp. 1–4, DOI:10.1109/DBTA.2010.5659030.
  • S. Abburu and G. S. Babu, “A cluster based multiple ontology parallel merge method,” in 2013 International Conference on Recent Trends in Information Technology (ICRTIT), 2013, pp. 335–40, DOI:10.1109/ICRTIT.2013.6844226.
  • X. Xu, G. M. Luis, A. Lobov, and J. L. Martinez Lastra, “Multiple ontology workspace management and performance assessment,” in 2015 IEEE 13th International Conference on Industrial Informatics (INDIN), 2015, pp. 1063–8, DOI:10.1109/INDIN.2015.7281882.
  • J. Yue, W. Mu, X. Liu, and Z. Fu, “Using protégé to construct vegetable SCM knowledge ontology,” in 2006 6th World Congress on Intelligent Control and Automation, 2006, pp. 7123–7, DOI:10.1109/WCICA.2006.1714467.
  • Z. Fu, J. Yue, D. Lin and X. Liu, “Ontology-based metadata model for agriculture E-commerce knowledge management,” in Third International Conference on Semantics, Knowledge and Grid (SKG 2007), 2007, pp. 616–17, DOI:10.1109/SKG.2007.139.
  • J. Fan, X. Zhang, and T. Dong, “Research of plant domain knowledge model based on ontology,” in 2008 3rd International Conference on Innovative Computing Information and Control, 2008, pp. 108–108, DOI:10.1109/ICICIC.2008.451.
  • D. Liying, L. Hongjuan, H. Dongbin, W. Zhe, and L. Xuning, “Research on intelligent searching of agricultural information based on ontology," in 2012 International Conference on Computer Science and Service System, 2012, pp. 1026–9, DOI:10.1109/CSSS.2012.260.
  • K. Phoksawat and M. Mahmuddin, “Ontology-based knowledge and optimization model for decision support system to intercropping,” in 2016 International Computer Science and Engineering Conference (ICSEC), 2016, pp. 1–6, DOI:10.1109/ICSEC.2016.7859927.
  • R. K. Kodali and A. Sahu, “An IoT based soil moisture monitoring on LosantPlatform,” in 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I), 2016, pp. 764–8, DOI:10.1109/IC3I.2016.7918063.
  • M. Dholu and K. A. Ghodinde, “Internet of Things (IoT) for precision agriculture application,” in 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI), IEEE, 2018, pp. 339–42, DOI:10.1109/ICOEI.2018.8553720.
  • G. Codeluppi, A. Cilfone, L. Davoli, and G. Ferrari, “VegIoT garden: A modular IoT management platform for urban vegetable gardens,” in IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), 2019, pp. 121–6, DOI:10.1109/MetroAgriFor.2019.8909228.
  • J. Gomez Gill, M. Martilnez Zarzuela2, F. J. Diaz Pernas, J. F. Diez Higuera, D. Gonzalez Ortega, and D. BotoGiralda, “Dynamic generation of fertilizer maps using GPS technology,” in 2005 IEEE Conference on Emerging Technologies and Factory Automation, 2005, pp. 667–72, DOI:10.1109/ETFA.2005.1612739.
  • L. Tan, R. Haley, R. Wortman, Y. Ampatzidis, and M. Whiting, “An integrated cloud-based platform for labor monitoring and data analysis in Precision agriculture,” in 2013 IEEE 14th International Conference on Information Reuse & Integration (IRI), 2013, pp. 349–56, DOI:10.1109/IRI.2013.6642492.
  • N. M. Cid-Garcia, V. Albornoz, Y. A. Rios-Solis, and R. Ortega, “Rectangular shape management zone delineation using integer linearprogramming,” Comput. Electron. Agri., Vol. 93, pp. 1–9, 2013.
  • G. L. A. Carrijo, D. E. Oliveira, G. A. de Assis, M. G. Carneiro, V. C. Guizilini, and J. R. Souza, “Automatic detection of fruits in coffee crops from aerial images,” in 2017 Latin American Robotics Symposium (LARS) and 2017 Brazilian Symposium on Robotics (SBR), 2017, pp. 1–6, DOI:10.1109/SBR-LARS-R.2017.8215283.
  • P. O. Skobelev, D. S. Budaev, G. Y. Voshchuk, A. N. Mochalkin, S. V. Susarev, and N. G. Gubanov, “Planning of production processes for agricultural enterprises using joint competences of university and IT company in development of knowledge bases,” in 2017 IEEE VI Forum Strategic Partnership of Universities and Enterprises of Hi-Tech Branches (Science. Education. Innovations) (SPUE), 2017, pp. 141–3, DOI:10.1109/IVForum.2017.8246074.
  • J. Dong, J. G. Burnham, B. Boots, G. Rains, and F. Dellaert, “4D crop monitoring: spatio-temporal reconstruction for agriculture,” in 2017 IEEE International Conference on Robotics and Automation (ICRA), 2017, pp. 3878–85, DOI:10.1109/ICRA.2017.7989447.
  • G. Sehgal, B. Gupta, K. Paneri, K. Singh, G. Sharma, and G. Shroff, “Crop Planning using stochastic visual optimization,” in 2017 IEEE Visualization in Data Science (VDS), 2017, pp. 47–51, DOI:10.1109/VDS.2017.8573443.
  • P. Lottes, J. Behley, A. Milioto, and C. Stachniss, “Fully convolutional networks with sequential information for robust crop and weed detection in precision farming,” IEEE Robotics Automation Lett., Vol. 3, no. 4, Oct. 2018.
  • Y. C. Kuang, L. Streeter, and M. J. Cree, “Evaluation of deep neural network and AlternatingDecision tree for kiwifruit detection,” in 2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC),2019, pp. 1–6, DOI:10.1109/I2MTC.2019.8826818.
  • G. K. Michelon, et al., “Software AgDataBox-Map to precision agriculture management,” SoftwareX, Vol. 10, pp. 1–7, 2019, DOI:10.1016/j.softx.2019.100320.
  • N. Li, X. Zhang, C. Zhang, H. Guo, Z. Sun, and A. X. Wu, “Real-time crop recognition in transplanted fields with prominent weed growth: A visual-attention-based approach,” IEEE Access, Vol. 7, pp. 185310–21, 2019, DOI:10.1109/ACCESS.2019.2942158.
  • C. O. Martinez-Ojeda, T. M. Amado, and J. C. Dela Cruz, “In field proximal soil sensing for real time crop recommendation using fuzzy logic model,” in 2019 International Symposium on Multimedia and Communication Technology (ISMAC), 2019, pp. 1–5, DOI:10.1109/ISMAC.2019.8836160.
  • C. Potena, R. Khanna, J. Nieto, R. Siegwart, D. Nardi, and A. Pretto, “Agricolmap: Aerial-ground collaborative 3D mapping for precision farming,” IEEE Robotics Automation Lett., Vol. 4, no. 2, pp. 1085–92, 2019.
  • K. O. Flores, L. M. Butaslac, J. E. M. Gonzales, S. M. G. Dumlao, and R. S. J. Reyes, “Precision agriculture monitoring systemusing wireless sensor network and raspberry Pi local server,” in 2016 IEEE Region 10 Conference (TENCON), 2016, pp. 3018–21, DOI:10.1109/TENCON.2016.7848600.
  • M. Saleh, I. H. Elhajj, D. Asmar, I. Bashour, and S. Kidess, “Experimental evaluation of low-cost resistive soil moisture sensors,” in 2016 IEEE International Multidisciplinary Conference on Engineering Technology (IMCET), 2016, 179–84, DOI:10.1109/IMCET.2016.7777448.
  • Z. Yang, W. Crow, L. Hu, L. Di, and R. Mueller, “Smap data for cropland soil moisture assessment: A case study,” in 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017, pp. 1996–9, DOI:10.1109/IGARSS.2017.8127373.
  • S. Marios and J. Georgiou, “Precision agriculture: Challenges in sensors and electronics for real-time soil and plant monitoring,” in 2017 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2017, pp. 1–4, DOI:10.1109/BIOCAS.2017.8325180.
  • G. Nithin Reddy, M. Danish, Y. SyamBabu, and G. Koperundevi, “Automatic irrigation and soil quality testing,” in 2018 International Conference on Recent Innovations in Electrical, Electronics & Communication Engineering (ICRIEECE), 2018, pp. 1363–7, DOI:10.1109/ICRIEECE44171.2018.9009114.
  • A. A. El-magrous, J. D. Sternhagen, G. Hatfield, and Q. Qiao, “Internet of things based weather-soil sensor station for precision agriculture,” in 2019 IEEE International Conference on Electro Information Technology (EIT), 2019, pp. 92–7, DOI:10.1109/EIT.2019.8833811.
  • I. Mednieks, “A method for correction of rural multispectral aerial image mosaics,” in 2012 13th Biennial Baltic Electronics Conference (BEC2012), October 3–5, 2012.
  • E. Bindlish, A. Lynn Abbott, and M. Balota, “Assessment of peanut Pod maturity”,” in 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), 2017, pp. 688–96, DOI:10.1109/WACV.2017.82.
  • M. P. Wachowiak, D. F. Walters, J. M. Kovacs, R. Wachowiak-Smolíková, and A. L. James, “Visual analytics and remote sensing imagery to support community-based research for precision agriculture in emerging areas,” Comput. Electron. Agric., Vol. 143, pp. 149–64, 2017.
  • Y. Xu, Z. Gao, L. Khot, X. Meng, and Q. Zhang, “A real-time weed mapping and precision herbicide spraying system for row crops,” Sensors, Vol. 18, p. 4245, 2018, DOI:10.3390/s18124245.
  • B. P. Vijaya Kumar, N. K. Mahadev Mohit, M. S. Pawan Ranjith, N. D. Nadig, and K. P. Nikita Menon, “Augmentation on satellite imagery with information integrated farming,” in 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), 2019, pp. 1–5, DOI:10.1109/ICECCT.2019.8869021.
  • S. G. Santos, J. C. Melo, R. G. Constantino, and A. V. Brito, “A solution for vegetation analysis, separation and geolocation of management zones using aerial images by UAVs,” in 2019 IX Brazilian Symposium on Computing Systems Engineering (SBESC), 2020, pp. 1–8, DOI:10.1109/SBESC49506.2019.9046079.
  • X. Chen and R. Roeber, “Monitoring soybean disease and insect infection patterns in Nebraska,” in 2005 IEEE International Conference on Granular Computing, 2005, pp. 128–30, DOI:10.1109/GRC.2005.1547250.
  • M. Zhang, M. Wang, L. Chen, S. S. Ang, C. V. Nguyen, and J. Zhu, “An automatic fluidic system for the rapid Detection of soil nutrients,” in 2008 IEEE International Conference on Automation and Logistics, 2008, pp. 2742–6, DOI:10.1109/ICAL.2008.4636639.
  • P. Venkata Krishna, S. Misra, S. Sivanesan, and M. S. Obaidat, “Learning automaton based context oriented middleware architecture for precision agriculture,” in 2015 International Conference on Computer, Information and Telecommunication Systems (CITS), 2015, pp. 1–5, DOI:10.1109/CITS.2015.7297720.
  • N. Gandhi and L. J. Armstrong, “Rice crop yield forecasting of tropical wet and dry climatic zone of India using data mining techniques,” in 2016 IEEE International Conference on Advances in Computer Applications (ICACA), 2016, pp. 357–63, DOI:10.1109/ICACA.2016.7887981.
  • K. Jedlička and K. Charvát, “Visualisation of big data in agriculture and rural development,” in IST-Africa week conference (IST-Africa), 2018, pp. 1–8.
  • T. M. Pinho, J. o. Paulo Coelho, J. Oliveira, and J. Boaventura-Cunha, “An overview on visual sensing for automaticcontrol on smart farming and forest management,” in 2018 13th APCA International Conference on Automatic Control and Soft Computing (CONTROLO), Jun. 4–6, 2018.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.