International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Smart Farmer: Deep Learning-Based Surveillance Application for Home Gardeners

M.D.S. WarnasooriyaDepartment of Information Technology, Faculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri LankaG.D.M. GodahewageDepartment of Information Technology, Faculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri LankaG.S. ManukalpaniDepartment of Information Technology, Faculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri LankaB.V.C. BhashiniDepartment of Information Technology, Faculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri LankaSuriyaa KumariDepartment of Information Technology, Faculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri LankaSupunya SwarnakanthaDepartment of Information Technology, Faculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka

Vol 7 No 6 (2023): Volume 7, Issue 6, June 2023 | Pages: 50-56

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 12-06-2023

doi Logo doi.org/10.47001/IRJIET/2023.706009

pdf iconFull Text PDF
Abstract

This paper presents a smart farming system designed for home farmers to identify deficiencies, pests, and weeds in their crops. The proposed system employs deep learning and image processing techniques to analyze images captured using a mobile phone camera. The system comprises four components, each responsible for identifying a specific type of damage. The isolated component is then analyzed using a deep learning model to determine the type of damage and provide remedial actions. The proposed system has the potential to improve crop health, increase yield, and reduce costs associated with ineffective remedial actions. The results of our experiments demonstrate the effectiveness of the proposed system in identifying and diagnosing crop damage.

Keywords

Image processing, Machine learning CNN, Deficiency identification, Pest damage identification, weed identification


Citation of this Article

M.D.S. Warnasooriya, G.D.M. Godahewage, G.S. Manukalpani, B.V.C. Bhashini, Suriyaa Kumari, Supunya Swarnakantha, “Smart Farmer: Deep Learning-Based Surveillance Application for Home Gardeners” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 7, Issue 6, pp 50-56, June 2023. Article DOI https://doi.org/10.47001/IRJIET/2023.706009

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
References
  1. D. H. Jung, T. I. Ahn, J. E. Son, and K. A. Sudduth, “Automated Lettuce Nutrient Solution Management Using an Array of Ion-Selective Electrodes,” Trans. ASABE, no. October, pp. 1309–1319, 2015.
  2. H. J. Kim, W. K. Kim, M. Y. Roh, C. I. Kang, J. M. Park, and K. A. Sudduth, “Automated sensing of hydroponic macronutrients using a computer-controlled system with an array of ion-selective electrodes,” Comput. Electron. Agric., vol. 93, pp. 46–54, 2013.
  3. T. Amirtha, T. Gokulalakshmi, P. Umamaheswari, and T. R. M. Tech, “Machine Learning Based Nutrient Deficiency Detection in Crops,” Int. J. Recent Technol. Eng., vol. 8, no. 6, pp. 5330–5333, 2020.
  4. M. Vassallo-Barco, L. Vives-Garnique, V. Tuesta-Monteza, H. I. Mejía-Cabrera, and R. Y. Toledo, “Automatic detection of nutritional deficiencies in coffee tree leaves through shape and texture descriptors,” J. Digit. Inf. Manag., vol. 15, no. 1, pp. 7–18, 2017.
  5. H. J. Butler, S. Adams, M. R. McAinsh, and F. L. Martin, “Detecting nutrient deficiency in plant systems using synchrotron Fourier-transform infrared microspectroscopy,” Vib. Spectrosc., vol. 90, pp. 46–55, 2017.
  6. W. J. Cho, H. J. Kim, D. H. Jung, D. W. Kim, T. I. Ahn, and J. E. Son, “On-site ion monitoring system for precision hydroponic nutrient management,” Comput. Electron. Agric., vol. 146, no. July 2017, pp. 51–58, 2018.
  7. A.Vyas, A. Mukherjee, and V. Kumar, "A deep learning approach for automatic detection and classification of plant diseases," Computers and Electronics in Agriculture, vol. 172, p. 105345, 2020.
  8. L. Zheng, D. Xiang, J. Wang, and J. Li, "A novel pest recognition method based on deep learning and attention mechanism," Computers and Electronics in Agriculture, vol. 185, p. 106023, 2021.
  9. A.Y. Pratama, W. H. Prasetiyo, and N. A. Setiawan, "Identification of pest damage in plants using deep learning and image processing," Journal of Physics: Conference Series, vol. 1841, no. 1, p. 012013, 2021.
  10. S.Gharibi, M. Niknejad, and A. Poursoltani, "Identification of pest-damaged almond leaves using deep learning and image processing techniques," Computers and Electronics in Agriculture, vol. 183, p. 106007, 2021.
  11. A.Aminu, A. A. Khan, and A. Raza, "Automatic identification and classification of agricultural pest images using deep learning," Computers and Electronics in Agriculture, vol. 182, p. 106004, 2021.
  12. Hossein Nejati, ZohrehAzimifar, Mohsen Zamani;“Using Fast Fourier Transform for weed detection in corn fields”; IEEE; 2008.
  13. Van Evert FK, Fountas S, Jakovetic D. Big data for weed control and crop protection. Weed Res. 2017; 57(4): 218–233.
  14. Weis M, Gerhards R. Feature extraction for the identification of weed species in digital images for the purpose of site specific weed control. In: Stafford JV, editor. Precision agriculture 007. Wageningen Academic Publishers; 2007.
  15. Ali A, Streibig JC, Christensen S, Andreasen C. Imagebased thresholds for weeds in maize fields. Weed Res. 2015; 55(1): 26–33.
  16. Zhang J, Song F, Tang J. Identification of crop weed based on image texture features. Comput Model New Technol.2014.
  17. Tang JL, Chen XQ, Miao RH, Wang D. Weed detection using image processing under different illumination for site-specific areas spraying. Comput Electron Agric. 2016.
  18. Breiman L. “Bagging predictors” Mach Learn 24:123–140, 1996.
  19. Freund Y, Schapire R, Abe N “A short introduction to boosting. J Jpn Soc ArtifIntell 14:1612’1999.
  20. Chen, CH., Tanaka, K., Kotera, M. et al. Comparison and improvement of the predictability and interpretability with ensemble learning models in QSPR applications. J Cheminform 12, 19 (2020).
  21. Amarasinghe, L. and B. Marambe. “Mimosa pigra L: A new weed in aquatic habitats of Sri Lanka." Sri Lankan J. Agric. Sci. 34:124-130, 1997.
  22. Pannacci E, Lattanzi B, Tei F. Non-chemical weed management strategies in minor crops: a review. Crop Prot.; 96: 44–58, 2017.
  23. Islam, N.; Rashid, M.M.; Pasandideh, F.; Ray, B.; Moore, S.; Kadel, R. “A Review of Applications and Communication Technologies for Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) Based Sustainable Smart Farming. Sustainability” p. 10, 2021.
  24. G. Huang∗, Y. Li∗, and G. Pleiss, “Snapshot ensembles: Train 1, Get M for free, Cornell University, Apr. 01, 2017.
  25. Loshchilov and F. Hutter, “SGDR: Stochastic gradient descent with warm restarts,” ICLR, 2017a.

Copyright @ 2026 IRJIET. All Right Reserved.

Licensed underCreative Commons Attribution 4.0 International License.