Abstract
In precision agriculture, the development of proximal imaging systems embedded in autonomous vehicles allows to explore new weed management strategies for site-specific plant application. Accurate monitoring of weeds while controlling wheat growth requires indirect measurements of leaf area index (LAI) and above-ground dry matter biomass (BM) at early growth stages. This article explores the potential of RGB images to assess crop-weed competition in a wheat (Triticum aestivum L.) crop by generating two new indicators, the weed pressure (WP) and the local wheat biomass production (δBMc). The fractional vegetation cover (FVC) of the crop and the weeds was automatically determined from the images with a SVM-RBF classifier, using bag of visual word vectors as inputs. It is based on a new vegetation index called MetaIndex, defined as a vote of six indices widely used in the literature. Beyond a simple map of weed infestation, the map of WP describes the crop-weed competition. The map of δBMc, meanwhile, evaluates the local wheat above-ground biomass production and informs us about a potential stress. It is generated from the wheat FVC because it is highly correlated with LAI (r2 = 0.99) and BM (r2 = 0.93) obtained by destructive methods. By combining these two indicators, we aim at determining whether the origin of the wheat stress is due to weeds or not. This approach opens up new perspectives for the monitoring of weeds and the monitoring of their competition during crop growth with non-destructive and proximal sensing technologies in the early stages of development.
Highlights
The emergence of proximal sensing technologies in precision agriculture provides new opportunities to drastically reduce chemical herbicides from site-specific weed management (SSWM) while maintaining production yield, quality, and commercial value [1,2,3]
The weed flora is composed of annual dicots such as Polygonum aviculare L. (EPPO code: POLAV), Fallopia convolvulus L. (POLCO), Capsella bursa-pastoris (L.) Medicus (CAPBP) and perennial dicots such as Convolvulus arvensis L. (CONAR)
We demonstrate the relevance of the machine-learning algorithm (SVM-RBF classifier) to estimate the fractional wheat vegetation cover (FVCc) and the use of visible images to estimate the leaf area index (LAI) and BM at early growth stages of wheat
Summary
The emergence of proximal sensing technologies in precision agriculture provides new opportunities to drastically reduce chemical herbicides from site-specific weed management (SSWM) while maintaining production yield, quality, and commercial value [1,2,3]. Imaging systems are mostly based on multi or hyperspectral optical sensors They require complex image processing algorithms to discriminate between crops and weeds and to generate weed maps [11,12,13]. The segmentation is generally performed using vegetation indices built as a combination of spectral bands. Their choice depends on the application: for RGB images, the most common is the excess green index (ExG or 2g-r-b index) proposed by Woebbecke et al [14]. With aerial images captured from an UAV, Torres-Sanchez et al [15] demonstrated that, among different vegetation indexes, the ExG and VEG indices performed best in vegetation fraction mapping. Our study focuses on the MetaIndex, a new vegetation index that takes advantage of six common indices
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