Development of a computational routine to evaluate the height of cotton plants

Abstract

Aerial photogrammetry with drones is being frequently used in agriculture. Since computational tools can optimize various personalized workflows for Digital Agriculture, the objective of this study was to develop modules (scripts) of a computational routine called FindPLANT-V1, leaving it personalized for testing on a preliminary basis, through products aerial photogrammetry obtained with a drone, the spatial and temporal variability of the height of cotton plants. To develop the study, an experimental area of ​​500 m2 was selected with irrigated cotton cultivation at the State University of Montes Claros (Unimontes) Experimental Farm, in Janaúba/MG. The FindPLANT-V1 routine has four modules, structured through object-oriented programming with Python 3.8 in the Spyder IDE. The following libraries were used to develop the scripts: Gdal, Imageio, Math, Matplolib, Numpy, OpenCV, OS, OSR, OGR, Pandas, Rasterstats and Scipy. A script was also developed to create the essential directories (inputs and outputs) for the operation of this routine. As a result of aerophotogrammetric geoprocessing, the average ground sample distances (GSD) of classic orthophotomosaics was 1.6 cm. The average GSD of the Digital Surface Model (DSM) and Digital Terrain Model (DTM) was 3.2 cm. Such products, routine inputs, made it possible to estimate the spatial and temporal variability (module 1.ST), the canopy height model (module 2.ProCHM), the zonal statistics of the plots (3.ZonalStat). The results of the routine developed, although partial, made it possible to analyze the spatial variability of cotton plants and quantify the height in different phases of the cycle, presenting flexibility of adaptation for herbaceous crops.