期刊
ENGINEERING TECHNOLOGY & APPLIED SCIENCE RESEARCH
卷 12, 期 4, 页码 8791-8795出版社
EOS ASSOC
关键词
image clustering; hybrid median image smoothing; IoT; robotics; agricultural applications
This study presents a method to remotely monitor and efficiently water agricultural fields to increase crop production. Advanced technologies such as image processing and neural networks were employed to study the plants' conditions and improve the quality of the image.
Adequately watering plants is a challenging task. Overand 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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