Digital Terrain Modelling by Remotely Piloted Aircraft: Optimization and Geometric Uncertainties in Precision Coffee Growing Projects

Abstract

The implantation of coffee crop plantations requires cartographic data for dimensioning areas and planning the planting line. Digital terrain models (DTMs) obtained from remotely piloted aircraft (RPA) can contribute to efficient data collection for topography making this technique applicable to precision coffee projects. Aiming to achieve efficiency in the collection, processing and photogrammetric products quality, flight configurations and image processing were evaluated. Two hundred sixty-five points obtained by Global Navigation Satellite System (GNSS) receivers characterized the topographic surface. Then eighteen flight missions were carried out by RPA in the configurations of altitude above ground level (AGL) and frontal and lateral image overlay. In addition, different point cloud formats evaluated the image processing (time) efficiency in DTM. Flights performed at 120 m AGL and 80 × 80% overlap showed higher assertiveness and efficiency in generation DTMs. The 90 m AGL flight showed great terrain detail, causing significant surface differences concerning the topography obtained by GNSS. An increase in image overlap requires longer processing times, not contributing linearly to the geometric quality of orthomosaic. Slope ranges up to 20% are considered reliable for precision coffee growing projects; above 20% overestimates the slope values of the land. Changes in flight settings and image processing are satisfactory for precision coffee projects. Image overlap reduction was significant in reducing the processing time without influencing the quality of the DTMs. In addition, image processing performed in shallow point clouds did not interfere with the DTMs quality.

Feasibility of Early Yield Prediction per Coffee Tree Based on Multispectral Aerial Imagery: Case of Arabica Coffee Crops in Cauca-Colombia

“…This study is oriented towards early prediction of coffee crop yields by using only multi-spectral image data obtained using low-cost tools. Multi-spectral images obtained from UAVs allow a high precision, focused analysis which, for this research, led to precise, automatic individualization of coffee trees and generated physical and spectral descriptors with the potential to predict crop yields [28,36,37].…”

Coffee Growing with Remotely Piloted Aircraft System: Bibliometric Review

“…Rosas et al [49] focused on applying low-cost materials for radiometric calibration of multispectral images. Santana et al [50] and Bento et al [51] evaluated the processing and quality of photogrammetric products based on different flight configurations and image processing obtained in coffee crops, aiming to achieve efficiency in field data collection. These last two studies emphasized that increased image overlap requires longer processing times and does not contribute linearly to the geometric quality of the orthomosaic.…”

Similarity Analysis between Contour Lines by Remotely Piloted Aircraft and Topography Using Hausdorff Distance: Application on Contour Planting

“…RPAs generate digital cartographic products, such as Digital Elevation Models (DEMs) [21,22] and slope maps [23,24], which are necessary for the discretization of the relief, where the farming will be installed. Such products are considered indispensable for several farmland management, e.g., coffee [25]. Coffee is generally growing on sloping lands and contour planting provides a well-distributed crop, increasing operational efficiency, mainly those arising from mechanized activity [25,26].…”

“…Such products are considered indispensable for several farmland management, e.g., coffee [25]. Coffee is generally growing on sloping lands and contour planting provides a well-distributed crop, increasing operational efficiency, mainly those arising from mechanized activity [25,26].…”