Weed Mapping with UAS Imagery and a Bag of Visual Words Based Image Classifier

Michael Pflanz,Henning Nordmeyer,Michael Schirrmann

doi:10.3390/rs10101530

Abstract

Weed detection with aerial images is a great challenge to generate field maps for site-specific plant protection application. The requirements might be met with low altitude flights of unmanned aerial vehicles (UAV), to provide adequate ground resolutions for differentiating even single weeds accurately. The following study proposed and tested an image classifier based on a Bag of Visual Words (BoVW) framework for mapping weed species, using a small unmanned aircraft system (UAS) with a commercial camera on board, at low flying altitudes. The image classifier was trained with support vector machines after building a visual dictionary of local features from many collected UAS images. A window-based processing of the models was used for mapping the weed occurrences in the UAS imagery. The UAS flight campaign was carried out over a weed infested wheat field, and images were acquired between a 1 and 6 m flight altitude. From the UAS images, 25,452 weed plants were annotated on species level, along with wheat and soil as background classes for training and validation of the models. The results showed that the BoVW model allowed the discrimination of single plants with high accuracy for Matricaria recutita L. (88.60%), Papaver rhoeas L. (89.08%), Viola arvensis M. (87.93%), and winter wheat (94.09%), within the generated maps. Regarding site specific weed control, the classified UAS images would enable the selection of the right herbicide based on the distribution of the predicted weed species.

Highlights

Weeds are in competition with crops for light, water, space, and nutrients [1,2,3], and can cause substantial crop yield losses [4,5,6]
The results showed that the Bag of Visual Words (BoVW) model allowed the discrimination of single plants with high accuracy for Matricaria recutita L. (88.60%), Papaver rhoeas L. (89.08%), Viola arvensis M. (87.93%), and winter wheat (94.09%), within the generated maps
We annotated a large set of images taken from a unmanned aerial vehicles (UAV) platform at different altitudes, and calibrated bag of visual words models based on scale-invariant feature transform (SIFT) image features and support vector machine classification

Summary

Introduction

Weeds are in competition with crops for light, water, space, and nutrients [1,2,3], and can cause substantial crop yield losses [4,5,6]. One strategy to reduce herbicide use and the related environmental impact is the use of site specific weed control (SSWM). The aim of SSWM is to confine the use of herbicides only to those areas where weeds are present in the fields, whilst avoiding reducing the efficacy of the overall crop protection application. 2018, 10, 1530 conditions [26,27] To some extent, these results could be corroborated under semi-controlled or outdoor conditions, under ambient lighting [26]. These results could be corroborated under semi-controlled or outdoor conditions, under ambient lighting [26] This would enable a pixel-wise acknowledgement of weed occurrences and may even detect weed leaves that only appear below crop plants, depending on the resolution of the images. It makes sense to further incorporate information about leaf shape, texture, and size in the classification

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