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Semantics Analysis of Agricultural Experts’ Opinions for Crop

Semantics Analysis of Agricultural Experts’ Opinions for Crop Productivity through Machine Learning

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Article: 2012055 | Received 24 Jun 2021, Accepted 22 Sep 2021, Published online: 14 Dec 2021

References

  • Abd-Elmabod, S. K., M. Muñoz-Rojas, A. Jordán, M. Anaya-Romero, J. D. Phillips, J. Laurence, Z. Zhang, P. Pereira, L. Fleskens, and M. van der Ploeg. 2020. Climate change impacts on agricultural suitability and yield reduction in a Mediterranean region. Geoderma 374: 114453. doi:10.1016/j.geoderma.2020.114453.
  • Abualigah, L. M., A. T. Khader, M. A. Al-Betar, and O. A. Alomari. 2017. Text feature selection with a robust weight scheme and dynamic dimension reduction to text document clustering. Expert Systems with Applications 84: 24–1001. doi:10.1016/j.eswa.2017.05.002.
  • Ahmad, K., and A. C. T. Heng. 2012. Determinants of agriculture productivity growth in Pakistan. International Research Journal of Finance and Economics 95: 163–173.
  • Angiani, G., L. Ferrari, T. Fontanini, P. Fornacciari, E. Iotti, F. Magliani, and S. Manicardi. 2016. A comparison between preprocessing techniques for sentiment analysis in Twitter. KDWeb 7 (2): 37–56.
  • Benos, L., A. C. Tagarakis, G. Dolias, R. Berruto, D. Kateris, and D. Bochtis. 2021. Machine Learning in agriculture: A comprehensive updated review. Sensors 21: 3758. doi:10.3390/s21113758.
  • Bontcheva, K., and D. Rout. 2014. Making sense of social media streams through semantics: A survey. Semantic Web 5: 373–403. doi:10.3233/SW-130110.
  • Chowdhury, G. G. 2003. Natural language processing. Annual Review of Information Science and Technology 37: 51–89. doi:10.1002/aris.1440370103.
  • Cortes, C., and V. Vapnik. 1995. Support-vector networks. Machine learning 20: 273–297.
  • Dai, X., Z. Zhuang, and P. X. Zhao. 2011. Computational analysis of miRNA targets in plants: Current status and challenges. Briefings in Bioinformatics 12: 115–21. doi:10.1093/bib/bbq065.
  • Dongare, M. 2020. Smart E-agriculture system using IoT and machine learning. Studies in Indian Place Names 40: 2486–93.
  • Elahi, E., Z. Khalid, C. Weijun, and H. Zhang. 2020. The public policy of agricultural land allotment to agrarians and its impact on crop productivity in Punjab province of Pakistan. Land Use Policy 90: 104324. doi:10.1016/j.landusepol.2019.104324.
  • Farooq, M. S., S. Riaz, A. Abid, T. Umer, and Y. B. Zikria. 2020. Role of IoT technology in agriculture: A systematic literature review. Electronics 9: 319. doi:10.3390/electronics9020319.
  • Haddi, E., X. Liu, and Y. Shi. 2013. The role of text pre-processing in sentiment analysis. Procedia Computer Science 17: 26–32. doi:10.1016/j.procs.2013.05.005.
  • Hmeidi, I., B. Hawashin, and E. El-Qawasmeh. 2008. Performance of KNN and SVM classifiers on full word Arabic articles. Advanced Engineering Informatics 22: 106–111. doi:10.1016/j.aei.2007.12.001.
  • Hoang, T.-A., W. W. Cohen, E.-P. Lim, D. Pierce, and D. P. Redlawsk. 2013. Politics, sharing and emotion in microblog. In IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Niagara Falls, ON, Canada, 282–89.
  • Hutto, C. J., and E. Gilbert. 2014. Vader: A parsimonious rule-based model for sentiment analysis of social media text. In Eighth international AAAI conference on weblogs and social media, Italy, 216–225.
  • Ikonomakis, M., S. Kotsiantis, and V. Tampakas. 2005. Text classification using machine learning techniques. WSEAS Transactions on Computers 4: 966–974.
  • Jayaraman, P. P., D. Palmer, A. Zaslavsky, and D. Georgakopoulos. 2015. Do-it-yourself digital agriculture applications with semantically enhanced IoT platform. In IEEE tenth international conference on intelligent sensors, sensor networks and information processing (IP), 1–6 IEEE, Singapore.
  • Kantasa-ard, A., M. Nouiri, A. Bekrar, A. Ait El Cadi, and Y. Sallez. 2020. Machine learning for demand forecasting in the physical internet: A case study of agricultural products in Thailand. International Journal of Production Research 1–25. doi:10.1080/00207543.2020.1844332.
  • Karthikeyan, P., K. Velswamy, P. Harshavardhanan, R. Rajagopal, V. JeyaKrishnan, and S. Velliangiri. 2020. Machine learning techniques application: Social media, agriculture, and scheduling in distributed systems. In Handbook of research on applications and implementations of machine learning techniques, 380–401. IGI Global, CMR Institute of Technology, India.
  • Kowsari, K., K. Jafari Meimandi, M. Heidarysafa, S. Mendu, L. Barnes, and D. Brown. 2019. Text classification algorithms: A survey. Information 10: 150. doi:10.3390/info10040150.
  • Li, H., H. Xiao, T. Qiu, and P. Zhou. 2013. Food safety warning research based on internet public opinion monitoring and tracing. In Second International Conference on Agro-Geoinformatics (Agro-Geoinformatics), 481–84. IEEE, Fairfax, VA, USA.
  • Liu, B, and L. Zhang. 2012. A survey of opinion mining and sentiment analysis mining text data. Springer 415–463.
  • Martini, D., E. Mietzsch, M. Schmitz, and M. Kunisch. 2011. The agriXchange platform as a means for coordination and support on data exchange in agriculture, ed. E. Gelb, and K. Charvt, vol. 11. EFITA/WCCA, Darmstadt, Germany.
  • Mekala, M. S., and P. Viswanathan. 2017. A survey: Smart agriculture IoT with cloud computing. In International conference on microelectronic devices, circuits and systems (ICMDCS), 1–7. IEEE, Vellore, India.
  • Mirończuk, M. M., and J. Protasiewicz. 2018. A recent overview of the state-of-the-art elements of text classification. Expert Systems with Applications 106: 36–54. doi:10.1016/j.eswa.2018.03.058.
  • Muharam, F. M., K. Nurulhuda, Z. Zulkafli, M. A. Tarmizi, A. N. H. Abdullah, M. F. Che Hashim, S. N. Mohd Zad, D. Radhwane, and M. R. Ismail. 2021. UAV-and Random-Forest-AdaBoost (RFA)-based estimation of rice plant traits. Agronomy 11: 915. doi:10.3390/agronomy11050915.
  • Prajapati, B. P., and D. R. Kathiriya. 2016. Evaluation of effectiveness of k-Means cluster based fast k-nearest neighbor classification applied on agriculture dataset. International Journal of Computer Science and Information Security 14: 800.
  • Prathibha, S., A. Hongal, and M. Jyothi. 2017. IoT based monitoring system in smart agriculture. In International Conference on Recent Advances in Electronics and Communication Technology (ICRAECT), 81–84. IEEE, Bangalore, India.
  • Razzaq, A., M. Asim, Z. Ali, S. Qadri, I. Mumtaz, D. M. Khan, and Q. Niaz. 2019. Text sentiment analysis using frequency-based vigorous features. China Communications 16: 145–153. doi:10.23919/JCC.2019.12.011.
  • Saif, H., M. Fernandez, Y. He, and H. Alani. 2013. 1st Interantional Workshop on Emotion and Sentiment in Social and Expressive Media: Approaches and Perspectives from AI (ESSEM 2013), 3 Dec 2013, Turin, Italy.
  • Saikai, Y., V. Patel, and P. D. Mitchell. 2020. Machine learning for optimizing complex site-specific management. Computers and Electronics in Agriculture 174: 105381. doi:10.1016/j.compag.2020.105381.
  • Singh, T., and M. Kumari. 2016. Role of text pre-processing in twitter sentiment analysis. Procedia Computer Science 89: 549–54. doi:10.1016/j.procs.2016.06.095.
  • Smith, M. J. 2018. Getting value from artificial intelligence in agriculture. Animal Production Scienc 60: 46–54. doi:10.1071/AN18522.
  • Soucy, P., and G. W. Mineau. 2001. A simple KNN algorithm for text categorization. In Proceedings International Conference on Data Mining, 647–48. IEEE, San Jose, CA, USA.
  • Tripathy, A., A. Agrawal, and S. K. Rath. 2015. Classification of sentimental reviews using machine learning techniques. Procedia Computer Science 57: 821–829. doi:10.1016/j.procs.2015.07.523.
  • Vaghela, V. B., B. M. Jadav, and M. Scholar. 2016. Analysis of various sentiment classification techniques. International Journal of Computer Applications 140: 0975–8887.
  • Wang, R., and J. Li. 2019. Bayes test of precision, recall, and F1 measure for comparison of two natural language processing models. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 4135–45, Florence, Italy.
  • Weiss, M., F. Jacob, and G. Duveiller. 2020. Remote sensing for agricultural applications: A meta-review. Remote Sensing of Environment 236: 111402.
  • Wolf, G. M. B. 1991. The beginnings of semantics. Essays, lectures and reviews. Duckworth London, Stanford University Press.
  • Xia, Y., and J. Wang. 2004. A one-layer recurrent neural network for support vector machine learning. Transactions on Systems, Man, and Cybernetics, Instt. Eelect. Elect. Eng. 34: 1261–1269.