An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery

TLDR

Accurate, timely detection of weeds between and within crop rows in early growth stages is a key challenge for site‑specific weed management, and higher spatial‑resolution imagery improves weed discrimination. The study develops an automatic OBIA algorithm using UAV imagery to create early post‑emergence prescription maps. The algorithm integrates DSMs, orthomosaics, and Random Forest classification, using OBIA‑derived plant heights as features and automatically selecting a class‑balanced training set to produce weed maps and prescription maps without manual training. The algorithm delivers rapid, accurate weed and prescription maps that enable timely, optimized herbicide application, thereby preventing yield loss.

Abstract

Accurate and timely detection of weeds between and within crop rows in the early growth stage is considered one of the main challenges in site-specific weed management (SSWM). In this context, a robust and innovative automatic object-based image analysis (OBIA) algorithm was developed on Unmanned Aerial Vehicle (UAV) images to design early post-emergence prescription maps. This novel algorithm makes the major contribution. The OBIA algorithm combined Digital Surface Models (DSMs), orthomosaics and machine learning techniques (Random Forest, RF). OBIA-based plant heights were accurately estimated and used as a feature in the automatic sample selection by the RF classifier; this was the second research contribution. RF randomly selected a class balanced training set, obtained the optimum features values and classified the image, requiring no manual training, making this procedure time-efficient and more accurate, since it removes errors due to a subjective manual task. The ability to discriminate weeds was significantly affected by the imagery spatial resolution and weed density, making the use of higher spatial resolution images more suitable. Finally, prescription maps for in-season post-emergence SSWM were created based on the weed maps—the third research contribution—which could help farmers in decision-making to optimize crop management by rationalization of the herbicide application. The short time involved in the process (image capture and analysis) would allow timely weed control during critical periods, crucial for preventing yield loss.