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

Abstract

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.

Coffee Growing with Remotely Piloted Aircraft System: Bibliometric Review

“…Bento et al [58] calculated the height and canopy diameter of newly transplanted coffee plants at three development periods, observed statistical differences between field measurements and aerial images, estimated linear equations between field data and aerial images, and monitored the temporal profile of growth and development of the studied cultivar in the field based on information extracted from aerial images using RPASs, with significant results compared to actual field data. Santos et al [59] aimed to identify which vegetation indices adequately explained plant chlorophyll and evaluated the relationships between vegetation indices obtained from RPAS images and leaf and canopy chlorophyll in coffee plants during the rainy and dry seasons. Also, focusing on chlorophyll estimation in coffee plants, Arteaga-López et al [60] identified the support vector machine model with the best performance and the CVI, GNDVI, and GCI vegetation indices with the best results.…”

Use of Images Obtained by Remotely Piloted Aircraft and Random Forest for the Detection of Leaf Miner (Leucoptera coffeella) in Newly Planted Coffee Trees

“…These studies aimed to mitigate challenges and improve the production of this crop in Brazil. RPA with multispectral sensors can fly over coffee plantations and capture high-resolution (in cm) images at different wavelengths, as highlighted in recent studies by Bento et al [9], Santana et al [10] and Santos et al [11] using such technologies for various applications in coffee farming. Such images can be processed to generate vegetation indices (VI), can provide information on the phytosanitary conditions of crops [12], be used to identify frost damage [13] and be used to identify diseases such as rust in coffee trees [14].…”