Monitoring and Analysis of Agricultural Field Parameters in Order to Increase Crop Yield through a Colored Object Tracking Robot, Image Processing, and IOT
Received: 29 April 2022 | Revised: 9 May 2022 | Accepted:15 May 2022 | Online: 7 August 2022
Corresponding author: S. M. Usha
Abstract
Adequately watering plants is a challenging task. Over- and under-watering may harm plants and seeds, as excess or restraint watering reduces crop production and yield. This study presents a method to remotely monitor and efficiently water agricultural fields to increase crop production by utilizing advanced technologies such as internet things, robotics, image processing, and neural networks. Accurate smoothing and image segmentation techniques were employed to study the plants' conditions. Color median, Gaussian, and hybrid median filters were employed to preprocess the data before segmentation and classification. The hybrid median filter and multilevel luminance grading system were employed to increase the quality of the image. The k-means clustering approach was used for image segmentation. The signal-to-noise ratios of the original and recreated images were compared and analyzed.
Keywords:
image clustering, hybrid median image smoothing, IoT, robotics, agricultural applicationsDownloads
References
S. Murawwat, A. Qureshi, S. Ahmad, and Y. Shahid, "Weed Detection Using SVMs," Engineering, Technology & Applied Science Research, vol. 8, no. 1, pp. 2412–2416, Feb. 2018. DOI: https://doi.org/10.48084/etasr.1647
S. A. B. Anas, R. S. S. Singh, and N. A. B. Kamarudin, "Designing an IoT Agriculture Monitoring System for Improving Farmer’s Acceptance of Using IoT Technology," Engineering, Technology & Applied Science Research, vol. 12, no. 1, pp. 8157–8163, Feb. 2022. DOI: https://doi.org/10.48084/etasr.4667
A. H. Blasi, M. A. Abbadi, and R. Al-Huweimel, "Machine Learning Approach for an Automatic Irrigation System in Southern Jordan Valley," Engineering, Technology & Applied Science Research, vol. 11, no. 1, pp. 6609–6613, Feb. 2021. DOI: https://doi.org/10.48084/etasr.3944
K. Khaskhoussy, B. Kahlaoui, B. M. Nefzi, O. Jozdan, A. Dakheel, and M. Hachicha, "Effect of Treated Wastewater Irrigation on Heavy Metals Distribution in a Tunisian Soil," Engineering, Technology & Applied Science Research, vol. 5, no. 3, pp. 805–810, Jun. 2015. DOI: https://doi.org/10.48084/etasr.563
N. Wang, N. Zhang, and M. Wang, "Wireless sensors in agriculture and food industry—Recent development and future perspective," Computers and Electronics in Agriculture, vol. 50, no. 1, pp. 1–14, Jan. 2006. DOI: https://doi.org/10.1016/j.compag.2005.09.003
D. D. Chaudhary, S. P. Nayse, and L. M. Waghmare, "Application of Wireless Sensor Networks for Greenhouse Parameter Control in Precision Agriculture," International Journal of Wireless & Mobile Networks, vol. 3, no. 1, pp. 140–149, Feb. 2011. DOI: https://doi.org/10.5121/ijwmn.2011.3113
M. Ilic, S. Ilic, S. Jovic, and S. Panic, "Early cherry fruit pathogen disease detection based on data mining prediction," Computers and Electronics in Agriculture, vol. 150, pp. 418–425, Jul. 2018. DOI: https://doi.org/10.1016/j.compag.2018.05.008
Md. R. Howlader, U. Habiba, R. H. Faisal, and Md. M. Rahman, "Automatic Recognition of Guava Leaf Diseases using Deep Convolution Neural Network," in 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), Cox’sBazar, Bangladesh, Oct. 2019, pp. 1–5. DOI: https://doi.org/10.1109/ECACE.2019.8679421
G. Sarp and M. Ozcelik, "Water body extraction and change detection using time series: A case study of Lake Burdur, Turkey," Journal of Taibah University for Science, vol. 11, no. 3, pp. 381–391, May 2017. DOI: https://doi.org/10.1016/j.jtusci.2016.04.005
S. H. Khan, X. He, F. Porikli, and M. Bennamoun, "Forest Change Detection in Incomplete Satellite Images With Deep Neural Networks," IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 9, pp. 5407–5423, Sep. 2017. DOI: https://doi.org/10.1109/TGRS.2017.2707528
H. Fathizad, M. A. Hakimzadeh Ardakani, R. T. Mehrjardi, and H. Sodaiezadeh, "Evaluating desertification using remote sensing technique and object-oriented classification algorithm in the Iranian central desert," Journal of African Earth Sciences, vol. 145, pp. 115–130, Sep. 2018. DOI: https://doi.org/10.1016/j.jafrearsci.2018.04.012
P. K. Mishra, A. Rai, and S. C. Rai, "Land use and land cover change detection using geospatial techniques in the Sikkim Himalaya, India," The Egyptian Journal of Remote Sensing and Space Science, vol. 23, no. 2, pp. 133–143, Aug. 2020. DOI: https://doi.org/10.1016/j.ejrs.2019.02.001
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, no. 12, pp. 5322–5328, Sep. 2017. DOI: https://doi.org/10.1109/JSTARS.2017.2746185
C. Huang, Y. Chen, S. Zhang, and J. Wu, "Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review," Reviews of Geophysics, vol. 56, no. 2, pp. 333–360, 2018. DOI: https://doi.org/10.1029/2018RG000598
C. A. Rishikeshan and H. Ramesh, "An automated mathematical morphology driven algorithm for water body extraction from remotely sensed images," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 146, pp. 11–21, Dec. 2018. DOI: https://doi.org/10.1016/j.isprsjprs.2018.08.014
E. Kampianakis, J. Kimionis, K. Tountas, C. Konstantopoulos, E. Koutroulis, and A. Bletsas, "Backscatter sensor network for extended ranges and low cost with frequency modulators: Application on wireless humidity sensing," in 2013 IEEE SENSORS, Baltimore, MD, USA, Aug. 2013, pp. 1–4. DOI: https://doi.org/10.1109/ICSENS.2013.6688469
A. Daccache, J. W. Knox, E. K. Weatherhead, A. Daneshkhah, and T. M. Hess, "Implementing precision irrigation in a humid climate – Recent experiences and on-going challenges," Agricultural Water Management, vol. 147, pp. 135–143, Jan. 2015. DOI: https://doi.org/10.1016/j.agwat.2014.05.018
S. Sreejith and J. Nayak, "Study of hybrid median filter for the removal of various noises in digital image," Journal of Physics: Conference Series, vol. 1706, no. 1, Sep. 2020, Art. no. 012079. DOI: https://doi.org/10.1088/1742-6596/1706/1/012079
M. R. Rakesh, B. Ajeya, and A. R. Mohan, "Hybrid Median Filter for Impulse Noise Removal of an Image in Image Restoration," International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 2, no. 10, pp. 5117–5124, Oct. 2013.
Downloads
How to Cite
License
Copyright (c) 2022 S. M. Usha, H. B. Mahesh
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
