Use of RPA Images in the Mapping of the Chlorophyll Index of Coffee Plants

• Published in: Sustainability 2022-10, Vol.14 (20), p.13118
• Main Authors: Santos, Luana Mendes dos, Ferraz, Gabriel Araújo e Silva, Carvalho, Milene Alves de Figueiredo, Teodoro, Sabrina Aparecida, Campos, Alisson André Vicente, Menicucci Neto, Pedro
• Format: Article
• Language: English
• Subjects: Agricultural commodities; Agriculture; Aircraft; Cameras; Chlorophyll; Coffee; Coffee industry; Crop diseases; Crops; Data collection; Decision making; Dry season; Farmers; Image processing; Inversion; Leaf area; Leaf area index; Leaves; Morphology; Nutritional status; Photosynthesis; Physiology; Pigments; Plantations; Plants (botany); Radiation; Rainy season; Remote sensing; Remotely piloted aircraft; Seasons; Sensors; Software; Spatial discrimination; Spatial resolution; Statistical analysis; Statistical tests; Sustainability; Vegetation
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su142013118

Coffee trading is an important source of income for the Brazilian commercial balance. Chlorophyll (Chl) are pigments responsible for converting radiation into energy; these pigments are closely related to the photosynthetic efficiency of plants, and the evaluation of the nutritional status of the coffee tree. The inversion method can be used for estimating the canopy chlorophyll content (Chlcanopy) using the leaf chlorophyll content (Chlleaf) and the leaf area index (LAI). The application of vegetation indices (VIs) in high spatial resolution images obtained from remotely piloted aircraft (RPA) can assist in the characterization of Chlcanopy in addition to providing vital and fast information for monitoring crops and aiding decision-making. This study aimed to identify which VIs adequately explain the Chl and evaluate the relationships between the VIs obtained from remotely piloted aircraft (RPA) images and the Chlleaf and Chlcanopy in coffee plants during the wet and dry seasons. The experiment was conducted on a Coffea arabica L. plantation in Lavras, Minas Gerais, Brazil. Images were collected on 26 November 2019 (wet), 11 August 2020 (dry), and 26 August 2021 (dry) by a multispectral camera embedded in a quadcopter. Plant height (H), crow diameter (D), and Chlleaf (a, b and total) data were collected in the field by a metre ruler (H and D) and sensor (Chlleaf). The LAI was calculated based on H and D. The Chlcanopy (a, b, and total) was calculated based on Chlleaf and LAI. The image processing was performed in Pix4D software, and postprocessing and calculation of the 21 VIs were performed in QGIS. Statistical analyses (descriptive, statistical tests, Pearson correlation, residuals calculation, and linear regression) were performed using the software R. The VIs from the RPA that best correlates to Chlcanopy in the wet season were the Modified Chlorophyll Absorption Ratio Index 2 (MCARI2RPA), Modified Simple Ratio (MSRRPA) and Simple Ratio (SRRPA). These VIs had high sensitivity and, therefore, were more affected by chlorophyll variability. For the two dry season studied days, there were no patterns in the relationships between Chlleaf, Chlcanopy, and the VIs. It was possible to use the Chl inversion method for the coffee during the wet season.