DETECÇÃO SEMIAUTOMÁTICA DE ÁRVORES EM POMAR DE MANGUEIRA IRRIGADA A PARTIR DE IMAGENS OBTIDAS POR DRONE

Abstract

The monitoring of the plant population in agricultural areas is essential to follow the productivity, assisting on the planning and decision making. Thus, our objective was to propose a protocol for remote detection of mango trees in the Low-Middle of the Sao Francisco River Valley, by using free software and plugins applied on aerial drone images. The study was carried out in three mango orchards. We used digital models extracted from orthomosaics created under three level of quality; then they were evaluated on the package QGIS with the plugins ‘Tree Density Calculator’ and ‘SAGA GIS’. The results were evaluated with the indices Precision, Recall and F1–Score. The precision index was higher for low quality processing; while the recall index showed higher values under medium and high quality, indicating that the higher the quality of the processing, the greater is the chance of acquiring an efficient tree counting. The highest F1–Score values were observed for the Tree Density Calculator plugin with low processing resolution. We recommend using this protocol for the remote identification and counting of mango trees, in a semi-automatic methodology through the use of aerial images obtained by UAVs and free software and plugins.