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

Ana De Castro,Jose Peña,Ovidiu Csillik,Francisca López-Granados,Francisco Jiménez-Brenes,Jorge Torres-Sánchez

doi:10.3390/rs10020285

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.

Highlights

In recent years, images acquired by Unmanned Aerial Vehicles (UAVs) have proven their suitability for the early weed detection in crops [1,2,3]
The success of UAV imagery for early season weed detection in herbaceous crops is based on three main factors: (1) images can be acquired at the most crucial agronomic moment for weed control due to the high flexibility of UAV flight scheduling; (2) orthomosaics generated from UAV images have very high spatial resolution because UAVs can fly at low altitudes, making the detection of plants possible, even at the earliest phenological stage; and (3) UAV imagery acquisition with high overlaps permits the generation of Digital Surface Models (DSMs) by using photo-reconstruction techniques [6]
The combination of UAV imagery and Object Based Image Analysis (OBIA) has enabled the significant challenge of automating early weed detection in early season herbaceous crops to be tackled [11,13], which represents a great advance in weed science

Summary

Introduction

Images acquired by Unmanned Aerial Vehicles (UAVs) have proven their suitability for the early weed detection in crops [1,2,3]. The success of UAV imagery for early season weed detection in herbaceous crops is based on three main factors: (1) images can be acquired at the most crucial agronomic moment for weed control (early post-emergence) due to the high flexibility of UAV flight scheduling; (2) orthomosaics generated from UAV images have very high spatial resolution (pixels < 5 cm) because UAVs can fly at low altitudes, making the detection of plants (crop and weeds) possible, even at the earliest phenological stage; and (3) UAV imagery acquisition with high overlaps permits the generation of Digital Surface Models (DSMs) by using photo-reconstruction techniques [6] Such UAV-based DSMs have recently been used for different objectives in herbaceous crops. Pérez-Ortiz et al [3,14] used a Support Vector Machine (SVM) to map weeds within the crop rows, user intervention for manual training patterns selection was required, which is time intensive and expensive, and could be a subjective task [15]

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Results

Conclusion