Preliminary study on the potential use of RPA images to quantify the influence of the defoliation after coffee harvesting to its yield
¹Federal University of Lavras, Department of Agricultural Engineering, University Campus, BR37.200-000 Lavras-MG, Brazil
²Embrapa Café, Brasília 70770-901, Distrito Federal, Brazil
³Federal University of Lavras, Department of Agronomy/ Phytotechnics, University Campus, BR37.200-000 Lavras-MG, Brazil
*Correspondence: luanna_mendess@yahoo.com.br
Abstract:
Coffee is an agricultural commodity with global commercial importance capable of impacting the production chain. The quantification of defoliation at harvest is important for monitoring crop yield because defoliation is one of the main types of damage caused by this agricultural operation in coffee crops. Thus, the objective of this study was to evaluate the relationship between yield and defoliation obtained in the field and obtained through remotely piloted aircraft (RPA) images. The experiment was conducted in a coffee plantation belonging to the Federal University of Lavras (UFLA), Lavras, Minas Gerais state, Brazil. An RPA with a rotary wing containing a multispectral camera was used in autonomous flight mode with a height of 30 m, an image overlap of 80%, and a speed of 3 m s-1. The images were collected before and after the 2020 and 2021 harvest, defoliation data obtained in the field were measured in 2020 and 2021, and the yield was measured from 2019 to 2021. Image processing was performed in the software PhotoScan, postimage processing was performed in QGIS, and statistical analyses were performed using the software R. With the processing of the images in 2020, the crop showed reductions of 17.3% and 18.4% in leaf area and volume, respectively, after harvest. In 2021, the crop showed reductions of 12.8% and 9.8% in leaf area and volume, respectively, after harvest. The leaf area and leaf volume of the coffee plantation after harvest could be quantified by means of images obtained by RPA, which allowed the observation of the loss of area and volume of the coffee plantation. Furthermore, it was possible to analyse the interactions between field data and the yield of the same harvest year, which were directly proportional, and the interaction of image data from one year with the previous yield, which were inversely proportional. In the year 2020, there was a reduction of 17.3% in leaf area after harvest, and a reduction of 18.4% in leaf volume after harvest in the plots under study.In the processing carried out in 2021, there was a 12.8% reduction in leaf area after harvest, and a 9.8% decrease in leaf volume after harvest in the plots under study.
Key words:
canopy volume, Coffea arabica L., digital image processing, harvest, systems of unmanned aircraft