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.
Monitoring of Coffee Tree Growth Through Crop Surface Models and MGRVI with Images Obtained with RPA / Ferraz, Gabriel Araújo e Silva; dos Santos, Luana Mendes; Andrade, Marco Thulio; Xavier, Letícia Aparecida Gonçalves; Maciel, Diogo Tubertini; Ferraz, Patrícia Ferreira Ponciano; Rossi, Giuseppe; Barbari, Matteo. - STAMPA. - (2020), pp. 757-763. [10.1007/978-3-030-39299-4_81]
Monitoring of Coffee Tree Growth Through Crop Surface Models and MGRVI with Images Obtained with RPA
Rossi, Giuseppe;Barbari, Matteo
2020
Abstract
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.File | Dimensione | Formato | |
---|---|---|---|
Coffee AIIA.pdf
Accesso chiuso
Tipologia:
Pdf editoriale (Version of record)
Licenza:
Tutti i diritti riservati
Dimensione
479.13 kB
Formato
Adobe PDF
|
479.13 kB | Adobe PDF | Richiedi una copia |
I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.