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      Non-destructive Measurement Methods of Sugarcane Canopy using Drone Technology

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      IOP Conference Series: Earth and Environmental Science
      IOP Publishing

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          Abstract

          Destructive methods in the field of leaf area measurement are not effective for predicting plant growth. The development of Unmanned Aerial Vehicle (UAV) and digital camera sensors makes it easier to obtain Small Format Aerial Photography (SFAP) data. Nondestructive sugarcane ( Saccharum officinarum) cutting using UAV technology is more effective than destructive methods. This study aims to determine the width of the tree canopy using Small-Format Aerial Photography Near-Infrared (NIR). The research stage includes a preliminary survey of sugarcane plantations and determination of Ground Control Point (GCP), data acquisition and SFAP using a modified Syma X8 Pro UAV drone and a Nikon Coolpix a 100 modified NIR camera, infrared aerial photo processing, measurement of cane area on sugarcane objects using Image Processing and Geographic Information Systems (GIS). The results of this research block sugarcane plantation SFAP in Balecatur Village, Sleman Regency, and a map of the area of sugarcane canopy in Balecatur Village, Sleman Regency. The cane canopy area of the study area is 29.3 m 2.

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          Applicability of Green-Red Vegetation Index for Remote Sensing of Vegetation Phenology

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            Computer vision technology in agricultural automation —A review

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              Monitoring plant condition and phenology using infrared sensitive consumer grade digital cameras

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                Author and article information

                Journal
                IOP Conference Series: Earth and Environmental Science
                IOP Conf. Ser.: Earth Environ. Sci.
                IOP Publishing
                1755-1307
                1755-1315
                April 01 2021
                April 01 2021
                : 752
                : 1
                : 012038
                Article
                10.1088/1755-1315/752/1/012038
                3a931cc4-e2ac-43cc-bd2d-752397d8a6c4
                © 2021

                http://creativecommons.org/licenses/by/3.0/

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