Multi-Terrain Automatic Controlled Bot Using Rgb-D Sensing Technology
Sania Afreen1, Manish Kumar2, Md. NavedKhan3, GurijalaVinayak4, Caroline El Fiorenza5
1Sania Afreen, Department of Computer Science Engineering, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
2Manish Kumar, Department of Computer Science Engineering, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
3Md. Naved Khan, Department of Computer Science Engineering, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
4GurijalaVinayak, Department of Computer Science Engineering, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
5Caroline El Fiorenza, Department of Computer Science Engineering, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
Manuscript received on 07 April 2019 | Revised Manuscript received on 20 April 2019 | Manuscript published on 30 April 2019 | PP: 758-761 | Volume-8 Issue-6, April 2019 | Retrieval Number: F3655048619/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Pixel-wise precision in farm scenes has reliable applications in the smart farming system. Deep learning methodology can enhance accuracy, especially under precision farming technique where the information from the depth map is initiated with low-resolution pictures. However, little research has been already done. We propose a theory for pixel-wise precision of different plant images based on deep learning using RGB-D images. We present in-deep fully convolutional neural network architecture for pixel-wise precision and to use the Continuously Adaptive MeanShift method which is based on RGB data when depth information is not sufficient to avoid false values due to low light. Experimental results show and prove the efficiency of the proposed method.
Keyword: Multi-Terrain Bot, Browser Controlled Bot, Smart Farming System, Precision Farming Bot, Cam Shit Method.
Scope of the Article: Remote Sensing, GIS and GPS