Monitoring of Coffee Tree Growth Through Crop Surface Models and MGRVI with Images Obtained with RPA
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For precision agriculture to monitor the crops during the vegetative and reproductive period is very important. Currently, remote sensing platforms such as remotely piloted aircraft (RPA) have stood out. The aim of this work was to evaluate the application of Modified Green Red Vegetation Index (MGRVI) vegetation index and Crop Surface Models (CSM) with images obtained by an RPA, to monitor the growth of coffee trees in three different seasons. The experiment was carried out at the Federal University of Lavras, Lavras, Minas Gerais, Brazil, in an area cultivated with coffee species Coffea arabica L. A RPA equipped with a digital camera was used to take photos, and Agisoft PhotoScan software was used to build the mosaic of photos and CSM. QGIS was used to obtain the height of the plants, application of the index MGRVI and the preparation of the map layouts by images processing. It was possible to identify the crop failure areas with the CSM. Crop Surface Models (CSM) showed to be a promising technique for the monitoring of coffee tree growth, making it possible to identify crop failures and growth variations. The MGRVI index failed to identify crop failures. The index did not recognize the difference between soil and vegetation, possibly due to the light variations in the area.
KeywordsPrecision agriculture Plant height Remote sensing UAS Vegetation index Crop monitoring
The authors thank, the Foundation for Research of the State of Minas Gerais (FAPEMIG) to funding this research, the National Council for Scientific and Technological Development (CNPq), the Coordination for the Improvement of Higher Education Personnel (CAPES), the Federal University of Lavras (UFLA) and University of Florence (UniFI).
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