[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Logo PTI Logo FedCSIS

Communication Papers of the 18th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 37

The Use of AI to Determine the Condition of Corn in a Field Robot that Meets the Requirements of Precision Farming

DOI: http://dx.doi.org/10.15439/2023F3649

Citation: Communication Papers of the 18th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 37, pages 319–327 ()

Full text

Abstract. Artificial intelligence helps to solve numerous problems in modern science and technology. AI-based image recognition allows the detection of specific features. One of the fields that uses AI-based image recognition is precision agriculture. The purpose of the solutions described in this article was to create a system based on artificial intelligence methods and use it in a real project. The article describes the methodology and results of work on tasks related to detection and recognition of corn growth stages, corn hydration levels, and detection and recognition of healthy corn and pathogen-infested corn. Details of the implementation, results and their usefulness for determining selected parameters of corn condition are presented. The developed system makes it possible to monitor the condition of corn, and can be extended to other crops in the future. The presented solution meets the requirements of precision agriculture and is in line with the idea of agriculture 4.0.

References

  1. Figiel, S. "Development of Artificial Intelligence and Potential Impact of Its Applications in Agriculture on Labor Use and Productivity/Rozwój sztucznej inteligencji i potencjalny wpływ jej zastosowań w rolnictwie na wykorzystanie siły roboczej i produktywność. zagadnienia ekonomiki Rolnej/problems of agricultural economics, 373 (4), 5–21." (2022). https://doi.org/10.30858/zer/153583
  2. Raj, E. Fantin Erudaya, M. Appadurai, and K. Athiappan. "Precision farming in modern agriculture." Smart Agriculture Automation Using Advanced Technologies: Data Analytics and Machine Learning, Cloud Architecture, Automation and IoT. Singapore: Springer Singapore, 2022. 61-87. http://dx.doi.org/10.1007/978-981-16-6124-2_4
  3. Shaikh, Tawseef Ayoub, Tabasum Rasool, and Faisal Rasheed Lone. "Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming." Computers and Electronics in Agriculture 198 (2022): 107119. https://doi.org/10.1016/j.compag.2022.107119
  4. Dharmaraj, V., and C. Vijayanand. "Artificial intelligence (AI) in agriculture." International Journal of Current Microbiology and Applied Sciences 7.12 (2018): 2122-2128. https://doi.org/10.20546/ijcmas.2018.712.241
  5. Bhat, Showkat Ahmad, and Nen-Fu Huang. "Big data and ai revolution in precision agriculture: Survey and challenges." IEEE Access 9 (2021): 110209-110222. https://doi.org/10.1109/ACCESS.2021.3102227
  6. Lorencowicz, Edmund. "Cyfrowe rolnictwo-cyfrowe zarządzanie." Roczniki Naukowe Stowarzyszenia Ekonomistów Rolnictwa i Agrobiznesu 20.4 (2018). https://doi.org/10.5604/01.3001.0012.2952
  7. Popović, Aleksandar, et al. "Current status and future prospects of organic cereal production in the world." Agro-knowledge Journal 18.3 (2018): 199-207. http://dx.doi.org/10.7251/AGREN1703199P
  8. Meier, Uwe & Bleiholder, Hermann & Buhr, Liselotte & Feller, Carmen & Hack, Helmut & Heß, Martin & Lancashire, Peter & Schnock, Uta & Stauß, Reinhold & Boom, Theo & Weber, Elfriede & Zwerger, Peter. (2009). The BBCH system to coding the phenological growth stages of plants-history and publications. Journal für Kulturpflanzen. 61. 41-52. http://dx.doi.org/10.5073/JfK.2009.02.01
  9. Skala BBCH dla kukurydzy, Publikacja specjalna magazynu rolniczego Agro Profil, PS_uprawa-kukurydzy.pdf (helion.pl)
  10. Acharya, R. (October 2020) Corn Leaf Infection Dataset, Version 1. Retrieved October 2020 from https://www.kaggle.com/qramkrishna/corn-leaf-infection-dataset.
  11. https://gradio.app/
  12. Tangwannawit, Panana, and Kanita Saengkrajang. "Technology acceptance model to evaluate the adoption of the internet of things for planting maize." Life Sciences and Environment Journal 22.2 (2021): 262-273. https://doi.org/10.14456/lsej.2021.13
  13. Prabha, R., et al. "Artificial intelligence-powered expert system model for identifying fall armyworm infestation in maize (Zea mays L.)." Journal of Applied and Natural Science 13.4 (2021): 1339-1349. https://doi.org/10.31018/jans.v13i4.3040
  14. Ahmad, Sajjad, Ajay Kalra, and Haroon Stephen. "Estimating soil moisture using remote sensing data: A machine learning approach." Advances in water resources 33.1 (2010): 69-80. https://doi.org/10.1016/j.advwatres.2009.10.008
  15. Cai, Yu, et al. "Research on soil moisture prediction model based on deep learning." PloS one 14.4 (2019): e0214508. https://doi.org/10.1371/journal.pone.0214508
  16. Adab, Hamed, et al. "Machine learning to estimate surface soil moisture from remote sensing data." Water 12.11 (2020): 3223. https://doi.org/10.3390/w12113223
  17. Achieng, Kevin O. "Modelling of soil moisture retention curve using machine learning techniques: Artificial and deep neural networks vs support vector regression models." Computers & Geosciences 133 (2019): 104320.v https://ui.adsabs.harvard.edu/link_gateway/2019CG....13304320A/http://dx.doi.org/10.1016/j.cageo.2019.104320
  18. Singh, Vijay Kumar, et al. "Modelling of soil permeability using different data driven algorithms based on physical properties of soil." Journal of Hydrology 580 (2020): 124223. https://doi.org/10.1016/j.jhydrol.2019.124223
  19. Kim, Myeong Hwan, and Chul Min Song. "Prediction of the Soil Permeability Coefficient of Reservoirs Using a Deep Neural Network Based on a Dendrite Concept." Processes 11.3 (2023): 661. https://doi.org/10.3390/pr11030661
  20. Tran, Van Quan. "Predicting and Investigating the Permeability Coefficient of Soil with Aided Single Machine Learning Algorithm." Complexity 2022 (2022). http://dx.doi.org/10.1155/2022/8089428
  21. Nagaraju, Mamillapally, and Priyanka Chawla. "Systematic review of deep learning techniques in plant disease detection." International journal of system assurance engineering and management 11 (2020): 547-560. http://dx.doi.org/10.1007/s13198-020-00972-1
  22. Kumar, M. Sunil, et al. "Deep Convolution Neural Network Based solution for Detecting Plant Diseases." Journal of Pharmaceutical Negative Results (2022): 464-471. http://dx.doi.org/10.47750/pnr.2022.13.S01.57
  23. Arora, Jatin, and Utkarsh Agrawal. "Classification of Maize leaf diseases from healthy leaves using Deep Forest." Journal of Artificial Intelligence and Systems 2.1 (2020): 14-26. https://doi.org/10.33969/AIS.2020.21002
  24. Paliwal, Jagrati, and Sunil Joshi. "An Overview of Deep Learning Models for Foliar Disease Detection in Maize Crop." Journal of Artificial Intelligence and Systems 4.1 (2022): 1-21. https://doi.org/ 10.17148/IJARCCE.2022.117104
  25. Proceedings of the 2022 Seventh International Conference on Research in Intelligent and Computing in Engineering, Vu Dinh Khoa, Shivani Agarwal, Gloria Jeanette Rincon Aponte, Nguyen Thi Hong Nga, Vijender Kumar Solanki, Ewa Ziemba (eds). ACSIS, Vol. 33, pages 177–182 (2022). http://dx.doi.org/10.15439/2022R34
  26. Proceedings of the 2022 Seventh International Conference on Research in Intelligent and Computing in Engineering, Vu Dinh Khoa, Shivani Agarwal, Gloria Jeanette Rincon Aponte, Nguyen Thi Hong Nga, Vijender Kumar Solanki, Ewa Ziemba (eds). ACSIS, Vol. 33, pages 227–234 (2022). http://dx.doi.org/10.15439/2022R08