Weed Detection and Mapping of a Coffee Farm by a Remotely Piloted Aircraft System

• Published in: Agronomy (Basel) Vol. 13; no. 3; p. 830
• Main Authors: Bento, Nicole Lopes, Ferraz, Gabriel Araújo e Silva, Amorim, Jhones da Silva, Santana, Lucas Santos, Barata, Rafael Alexandre Pena, Soares, Daniel Veiga, Ferraz, Patrícia Ferreira Ponciano
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-03-2023
• Subjects: Agricultural production; Agricultural wastes; Agriculture; Agrochemicals; Aircraft; Algorithms; Classification; Coffee; Coffee industry; Crops; Cultivars; Data analysis; Differentiation; digital agriculture; Drone aircraft; Environmental impact; Image processing; International economic relations; Machine learning; multispectral images; Plantations; precision coffee farming; remote sensing; Remote sensors; Remotely piloted aircraft; Sensors; Software; Spraying; Support vector machines; Weeds; Brazil; United States > US
• ISSN: 2073-4395; 2073-4395
• DOI: 10.3390/agronomy13030830

The differentiation between the main crop and weeds is an important step for selective spraying systems to avoid agrochemical waste and reduce economic and environmental impacts. In this sense, this study aims to classify and map the area occupied by weeds, determine the percentage of area occupied, and indicate treatment control strategies to be adopted in the field. This study was conducted by using a yellow Bourbon cultivar (IAC J10) with 1 year of implementation on a commercial coffee plantation located at Minas Gerais, Brazil. The aerial images were obtained by a remotely piloted aircraft (RPA) with an embedded multispectral sensor. Image processing was performed using PIX4D, and data analysis was performed using R and QGIS. The random forest (RF) and support vector machine (SVM) algorithms were used for the classification of the regions of interest: coffee, weed, brachiaria, and exposed soil. The differentiation between the study classes was possible due to the spectral differences between the targets, with better classification performance using the RF algorithm. The savings gained by only treating areas with the presence of weeds compared with treating the total study area are approximately 92.68%.