RGB Image-Derived Indicators for Spatial Assessment of the Impact of Broadleaf Weeds on Wheat Biomass

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.

IPSIM-Cirsium, a Qualitative Expert-Based Model to Predict Infestations of Cirsium arvense

“…However, to be able to express weed harmfulness in the field, it is necessary to describe its spatial distribution in the field according to patches for C. arvense. A relationship between the mapping of Cirsium arvense shoots and their impact on yield loss has been established for a few specific crops (Gee and Denimal, 2020;Rasmussen and Nielsen, 2020). Representing the patches of Cirsium arvense is not possible in IPSIM-Cirsium and the choice of representation of the infestation was done according to weed pressure.…”

Assessment of the Electrostatic Separation Effectiveness of Plastic Waste Using a Vision System

“…The mean volume estimation error was about 3%. Based on the RGB images, the biomass quality was also assessed in the article [ 27 ].…”

CNN Based Automated Weed Detection System Using UAV Imagery

“…Reference [2] achieved an accuracy ranging between 70.3% to 82.3% for the weed classification; however, the significant limitations of this study were the lower spatial resolution of the images used (10 m) and static data splitting ratio of 1:3 and 3:1. Another study by [3] used SVM with radial basis function (RBF) to classify broadleaf weeds using UAV images and achieved an overall accuracy of 93%. The limitation of [3] was the imbalanced dataset used in the investigation with 254 images for middle-tillering and six images for end tillering and stem extension.…”

“…Another study by [3] used SVM with radial basis function (RBF) to classify broadleaf weeds using UAV images and achieved an overall accuracy of 93%. The limitation of [3] was the imbalanced dataset used in the investigation with 254 images for middle-tillering and six images for end tillering and stem extension. A study [4] applied SVM for weed classification using a ground-based camera and achieved an accuracy of 97.3%.…”