Weeds detection in a Citrus orchard using multispectral UAV data and machine learning algorithms: A case study from Souss-Massa basin, Morocco

Abstract

Recently, Unmanned Aerial Vehicles (UAVs) have been used extensively in agriculture, especially at the farm scale. In this work, the images acquired using the DJI Phantom 4 Pro Multispectral (P4M) were used to detect weeds taking two sites in a Citrus orchard farm located in the Souss-Massa region as a case study. A variety of processing steps were employed to prepare the data. Starting by the image's alignment, followed by the georeferencing using ground control points (GCPs), the creation of dense clouds, generation of the digital elevation model (DEM), digital surface model (DSM) and finishing by the extraction of the orthomosaic and multispectral image. Then, the spectral indices including the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) were calculated. And the machine learning (ML) algorithms such as Maximum Likelihood Classification (MLC), Mahalanobis Distance Classification (MHDC), Minimum Distance Classification (MDC), Spectral Angle Mapper (SAM), and Support Vector Machine (SVM) were applied. In the site 1, the results showed an overall accuracy of 90.00 %, 70.50 %, 76.25 %, 57.50 %, and 88.48 %, and the Cohen's kappa coefficient of 0.81, 0.79, 0.63, 0.44, and 0.77 for the MLC, MHLC, MDC, SAM, and SVM respectively. In the site 2, the results showed an overall accuracy of 97.05 %, 94.12 %, 89.70 %, 52.94 %, 66.33 %, and the Cohen's kappa coefficient of 0.95, 0.90, 0.83, 0.39, and 0.43 for the MLC, MHLC, MDC, SAM, and SVM respectively. This study has therefore shown the potential of UAVs data, and the opportunity that presents this new technology for farmers to develop their production and optimize the water and fertilizers consumption.