International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
권호 표지
DOI QR코드

DOI QR Code

A Comprehensive Literature Study on Precision Agriculture: Tools and Techniques

  • Bh., Prashanthi (Department of CSE, Koneru Lakshmaiah Education Foundation and CSE, GRIET) ;
  • A.V. Praveen, Krishna (Department of CSE, Koneru Lakshmaiah Education Foundation) ;
  • Ch. Mallikarjuna, Rao (Department of CSE, GRIET)
  • 투고 : 2022.12.05
  • 발행 : 2022.12.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Due to digitization, data has become a tsunami in almost every data-driven business sector. The information wave has been greatly boosted by man-to-machine (M2M) digital data management. An explosion in the use of ICT for farm management has pushed technical solutions into rural areas and benefited farmers and customers alike. This study discusses the benefits and possible pitfalls of using information and communication technology (ICT) in conventional farming. Information technology (IT), the Internet of Things (IoT), and robotics are discussed, along with the roles of Machine learning (ML), Artificial intelligence (AI), and sensors in farming. Drones are also being studied for crop surveillance and yield optimization management. Global and state-of-the-art Internet of Things (IoT) agricultural platforms are emphasized when relevant. This article analyse the most current publications pertaining to precision agriculture using ML and AI techniques. This study further details about current and future developments in AI and identify existing and prospective research concerns in AI for agriculture based on this thorough extensive literature evaluation.

키워드

참고문헌

  1. F. Bu and X. Wang, "A smart agriculture IoT system based on deep reinforcement learning," Future Generation Computer Systems, vol. 99, pp. 500-507, 2019.  https://doi.org/10.1016/j.future.2019.04.041
  2. S. AlZu'bi, B. Hawashin, M. Mujahed, Y. Jararweh, and B. B. Gupta, "An efficient employment of internet of multimedia things in smart and future agriculture," Multimedia Tools and Applications, vol. 78, pp. 29581-29605, 2019.  https://doi.org/10.1007/s11042-019-7367-0
  3. C. Kishore Kumar and V. Venkatesh, "Design and development of IOT based intelligent agriculture management system in greenhouse environment," International Journal of Engineering and Advanced Technology, vol. 8, pp. 47-52, 2019.  https://doi.org/10.35940/ijeat.e1006.0585c19
  4. C. Hu, X. Liu, Z. Pan, and P. Li, "Automatic Detection of Single Ripe Tomato on Plant Combining Faster R-CNN and Intuitionistic Fuzzy Set," IEEE Access, vol. 7, pp. 154683-154696, 2019.  https://doi.org/10.1109/access.2019.2949343
  5. A. V. P. Krishna, A. M. Srinaga, R. A. Kumar, R. Nachiketh and V. V. Vardhan, "Planning Secure Consumption: Food Safety Using Blockchain," 2021 IEEE International Conference on Technology, Research, and Innovation for Betterment of Society (TRIBES), 2021, pp. 1-5, doi: 10.1109/TRIBES52498.2021.9751659.. 
  6. A. Chlingaryan, S. Sukkarieh, and B. Whelan, "Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review," Computers and Electronics in Agriculture, vol. 151, pp. 61-69, Aug 2018.  https://doi.org/10.1016/j.compag.2018.05.012
  7. Alves, G.M., Cruvinel, P.E., 2016. Big data environment for agricultural soil analysis from ct digital images. In: 2016 IEEE Tenth International Conference on Semantic Computing (ICSC). IEEE, pp. 429-431. 
  8. Aubert, B.A., Schroeder, A., Grimaudo, J., 2012. IT as enabler of sustainable farming: An empirical analysis of farmers' adoption decision of precision agriculture technology. Decision Support Systems 54 (1), 510-520.  https://doi.org/10.1016/j.dss.2012.07.002
  9. Griffin, T.W., Miller, N.J., Bergtold, J., Shanoyan, A., Sharda, A., Ciampitti, I.A., 2017. Farm's sequence of adoption of information-intensive precision agricultural technology. Applied Engineering in Agriculture 33 (4), 521.  https://doi.org/10.13031/aea.11786
  10. Hedley, C., 2015. The role of precision agriculture for improved nutrient management on farms. Journal of the Science of Food and Agriculture 95 (1), 12-19.  https://doi.org/10.1002/jsfa.6734
  11. Hernandez, A., Murcia, H., Copot, C., De Keyser, R., 2015. Towards the development of a smart flying sensor: illustration in the field of precision agriculture. Sensors 15(7), 16688-16709  https://doi.org/10.3390/s150716688
  12. Atole, R. R. and Park, D. (2018), 'A multiclass deep convolutional neural network classifier for detection of common rice plant anomalies', INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 9(1), 67-70 
  13. Bashir, K., Rehman, M. and Bari, M. (2019), 'Detection and classification of rice diseases: An automated approach using textural features', Mehran University Research Journal of Engineering and Technology 38(1), 239-250  https://doi.org/10.22581/muet1982.1901.20
  14. Bera, T., Das, A., Sil, J. and Das, A. K. (2019), A survey on rice plant disease identification using image processing and data mining techniques, in 'Emerging Technologies in Data Mining and Information Security', Springer, pp. 365-376. 
  15. Chaudhary, P., Chaudhari, A. K., Cheeran, A. and Godara, S. (2012), 'Color transform based approach for disease spot detection on plant leaf', International Journal of Computer Science and Telecommunications 3(6), 65-70. 
  16. Suciu, George, Ioana Marcu, Cristina Balaceanu, Marius Dobrea, and Elena Botezat. "Efficient IoT system for precision agriculture." In 2019 15th International Conference on Engineering of Modern Electric Systems (EMES), pp. 173-176. IEEE, 2019. 
  17. Wiangtong, Theerayod, and Phaophak Sirisuk. "IoT-based versatile platform for precision farming." In 2018 18th International Symposium on Communications and Information Technologies (ISCIT), pp. 438-441. IEEE, 2018. 
  18. Saraf, Shweta B., and Dhanashri H. Gawali. "IoT based smart irrigation monitoring and controlling system." In 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), pp. 815-819. IEEE, 2017. 
  19. Anitha, A., Nithya Sampath, and M. Asha Jerlin. "Smart Irrigation system using Internet of Things." In 2020 Interntional Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), pp. 1-7. IEEE, 2020. 
  20. Laksiri, H. G. C. R., H. A. C. Dharmagunawardhana, and V.Wijayakulasooriya. "Design and Optimization of IoT Based Smart Irrigation System in Sri Lanka." In 2019 14th Conference on Industrial and Information Systems (ICIIS), pp. 198-202. IEEE, 2019. 
  21. A. Chlingaryan, S. Sukkarieh, and B. Whelan, "Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review," Computers and Electronics in Agriculture, vol. 151, pp. 61-69, Aug 2018.  https://doi.org/10.1016/j.compag.2018.05.012
  22. C. Sun, "Using of Multi-Source and Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region," Hortic Res, vol. 19, May 26 2019. 
  23. C. Yang, J. H. Everitt, Q. Du, B. Luo, and J. Chanussot, "Using High-Resolution Airborne and Satellite Imagery to Assess Crop Growth and Yield Variability for Precision Agriculture," Proceedings of the IEEE, vol. 101, pp. 582-592, 2013  https://doi.org/10.1109/JPROC.2012.2196249
  24. F. Tseng, H. Cho, and H. Wu, "Applying Big Data for Intelligent Agriculture-Based Crop Selection Analysis," IEEE Access, vol. 7, pp. 116965-116974, 2019.  https://doi.org/10.1109/access.2019.2935564
  25. D. Murugan, A. Garg, and D. Singh, "Development of an Adaptive Approach for Precision Agriculture Monitoring with Drone and Satellite Data," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 10, pp. 5322-5328, 2017.  https://doi.org/10.1109/jstars.2017.2746185
  26. Kingsley Eghonghon Ukhurebor, Charles Oluwaseun Adetunji, Olaniyan T. Olugbemi, W. Nwankwo, Akinola Samson Olayinka, C. Umezuruike, Daniel Ingo Hefft, Chapter 6 - Precision agriculture: Weather forecasting for future farming, In Intelligent Data-Centric Systems, AI, Edge and IoT-based Smart Agriculture, Academic Press, 2022, Pages 101-121, ISBN 9780128236949, 
  27. Bagha, H.; Yavari, A.; Georgakopoulos, D. Hybrid Sensing Platform for IoT-Based Precision Agriculture. Future Internet 2022, 14, 233. https://doi.org/10.3390/fi14080233 
  28. Bhanu, K. N., H. S. Mahadevaswamy, and H. J. Jasmine. "IoT based Smart System for Enhanced Irrigation in Agriculture." In 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), pp. 760-765. IEEE, 2020. 
  29. Grimblatt, Victor, Guillaume Ferre, Francois Rivet, Christophe Jego, and Nicolas Vergara. "Precision agriculture for small to medium size farmers-an IoT approach." In 2019 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1-5. IEEE, 2019. 
  30. Y. Ampatzidis, V. Partel, B. Meyering, and U. Albrecht, "Citrus rootstock evaluation utilizing UAV-based remote sensing and artificial intelligence," Computers and Electronics in Agriculture, vol. 164, 2019 
  31. A. Catini, "Development of a Sensor Node for Remote Monitoring of Plants," Sensors (Basel), vol. 19, Nov 8 2019. 
  32. W. Wu, A.-D. Li, X.-H. He, R. Ma, H.-B. Liu, and J.-K. Lv, "A comparison of support vector machines, artificial neural network and classification tree for identifying soil texture classes in southwest China," Computers and Electronics in Agriculture, vol. 144, pp. 86-93, 2018.  https://doi.org/10.1016/j.compag.2017.11.037
  33. A. D. Mengistu and D. M. Alemayehu, "Soil Characterization and Classification: A Hybrid Approach of Computer Vision and Sensor Network.," International Journal of Electrical & Computer Engineering (2088-8708), vol. 8, no. 2, 2018. 
  34. I. Ymeti, H. Van der Werff, D. P. Shrestha, V. G. Jetten, C. Lievens, and F. Van der Meer, "Using color, texture and object-based image analysis of multitemporal camera data to monitor soil aggregate breakdown," Sensors, vol. 17, no. 6, p. 1241, 2017. 
  35. S. K. Honawad, S. S. Chinchali, K. Pawar, and P. Deshpande, "Soil classification and suitable crop prediction," in National Conference On Advances In Computational Biology, Communication, And Data Analytics, 2017, pp. 25-29. 
  36. K. Srunitha and S. Padmavathi, "Performance of SVM classifier for image based soil classification," in 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), 2016, pp. 411-415. 
  37. Y. Guang, Q. Shujun, C. Pengfei, D. Yu, and T. Di, "Rock and soil classification using PLS-DA and SVM combined with a laser-induced breakdown spectroscopy Library," Plasma Science and Technology, vol. 17, no. 8, p. 656, 2015. 
  38. A. D. Vibhute, K. V. Kale, R. K. Dhumal, and S. C. Mehrotra, "Soil type classification and mapping using hyperspectral remote sensing data," in 2015 International Conference on Man and Machine Interfacing (MAMI), 2015, pp. 1-4. 
  39. A. Fuentes, S. Yoon, S. C. Kim, and D. S. Park, "A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition," Sensors, vol. 17, no. 9, p. 2022, 2017. 
  40. J. Ma, K. Du, F. Zheng, L. Zhang, Z. Gong, and Z. Sun, "A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network," Computers and electronics in agriculture, vol. 154, pp. 18-24, 2018.  https://doi.org/10.1016/j.compag.2018.08.048
  41. B. A. Ashqar and S. S. Abu-Naser, "Image-Based Tomato Leaves Diseases Detection Using Deep Learning," 2019. 
  42. K. P. Ferentinos, "Deep learning models for plant disease detection and diagnosis," Computers and Electronics in Agriculture, vol. 145, pp. 311-318, 2018.  https://doi.org/10.1016/j.compag.2018.01.009
  43. J. Amara, B. Bouaziz, and A. Algergawy, "A deep learning-based approach for banana leaf diseases classification," Datenbanksysteme fur Business Technologie und Web (BTW 2017)-Workshopband, 2017. 
  44. A. Ramcharan, K. Baranowski, P. McCloskey, B. Ahmed, J. Legg, and D. P. Hughes, "Deep learning for image-based cassava disease detection," Frontiers in plant science, vol. 8, p. 1852, 2017. 
  45. D. Oppenheim and G. Shani, "Potato disease classification using convolution neural networks," Advances in Animal Biosciences, vol. 8, no. 2, pp. 244-249, 2017.  https://doi.org/10.1017/S2040470017001376
  46. S. Sladojevic, M. Arsenovic, A. Anderla, D. Culibrk, and D. Stefanovic, "Deep neural networks based recognition of plant diseases by leaf image classification,"Computational intelligence and neuroscience, vol. 2016, 2016. 
  47. S. P. Mohanty, D. P. Hughes, and M. Salathe, "Using deep learning for image based plant disease detection," Frontiers in plant science, vol. 7, p. 1419, 2016.. 
  48. Bhanu, K. N., H. S. Mahadevaswamy, and H. J. Jasmine. "IoT based Smart System for Enhanced Irrigation in Agriculture." In 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), pp. 760-765. IEEE, 2020. 
  49. Laksiri, H. G. C. R., H. A. C. Dharmagunawardhana, and V.Wijayakulasooriya. "Design and Optimization of IoT Based Smart Irrigation System in Sri Lanka." In 2019 14th Conference on Industrial and Information Systems (ICIIS), pp. 198-202. IEEE, 2019. 
  50. Saraf, Shweta B., and Dhanashri H. Gawali. "IoT based smart irrigation monitoring and controlling system." In 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), pp. 815-819. IEEE, 2017.  
  51. Suciu, George, Ioana Marcu, Cristina Balaceanu, Marius Dobrea, and Elena Botezat. "Efficient IoT system for precision agriculture." In 2019 15th International Conference on Engineering of Modern Electric Systems (EMES), pp. 173-176. IEEE, 2019 
  52. Marcu, Ioana M., George Suciu, Cristina M. Balaceanu, and Alexandru Banaru. "IoT based System for Smart Agriculture." In 2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), pp. 1-4. IEEE, 2019. 
  53. Dolci, Rob. "IoT solutions for precision farming and food manufacturing: artificial intelligence applications in digital food." In 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC), vol. 2, pp. 384-385. IEEE, 2017