Monitoring and Analysis of Agricultural Field Parameters in Order to Increase Crop Yield through a Colored Object Tracking Robot, Image Processing, and IOT
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...
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Published in | Engineering, technology & applied science research Vol. 12; no. 4; pp. 8791 - 8795 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
D. G. Pylarinos
01.08.2022
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Subjects | |
Online Access | Get full text |
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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. |
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AbstractList | 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. |
Author | Mahesh, H. B. Usha, S. M. |
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Cites_doi | 10.48084/etasr.1647 10.1109/ICSENS.2013.6688469 10.1016/j.ejrs.2019.02.001 10.1016/j.agwat.2014.05.018 10.48084/etasr.3944 10.1016/j.isprsjprs.2018.08.014 10.48084/etasr.563 10.48084/etasr.4667 10.5121/ijwmn.2011.3113 10.1088/1742-6596/1706/1/012079 10.1109/ECACE.2019.8679421 10.1029/2018RG000598 10.1109/TGRS.2017.2707528 10.1016/j.jtusci.2016.04.005 10.1109/JSTARS.2017.2746185 10.1016/j.compag.2018.05.008 10.1016/j.jafrearsci.2018.04.012 10.1016/j.compag.2005.09.003 |
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SubjectTerms | agricultural applications hybrid median image smoothing image clustering iot robotics |
Title | Monitoring and Analysis of Agricultural Field Parameters in Order to Increase Crop Yield through a Colored Object Tracking Robot, Image Processing, and IOT |
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