Weed control is essential for crop quality and yield. Usually, the main factors affectingdecision-making of farmers in weeding are based on the presence of certain weedvarieties and their density (plants/m2). However, the weed management at the scale ofthe crop cycle refers to other competition indicators, more reliable, such as plantbiomass or leaf area index (LAI). Classically, plant biomass measurements are obtainedby a destructive method of sampling, time consuming and it turns out to be unworkablein practice for farmers. This project proposes to study an innovative method of biomassevaluation which is based on the acquisition of plot images to determine the leaf areaindex at young stage. An ecophysiologial model (the crop-growth model Azodyn) wasindirectly fed with visible (RGB) images, considered as input parameters, to predict thetemporal evolution of the plant biomass until a date close to the acquisition date. First,the model is tested on wheat crop where projected leaf area index (PLAI) of both weedand wheat is determined from image processing. The PLAI is compared to classicalmeasurements (leaf area index and aerial biomass) and a good correlation between theaerial biomass estimated with images and classical biomass measurements is obtained.Second, biomass prediction of the global weed community is estimated from the modelusing as input parameter initial aerial biomass derived from PLAI-aerial biomasscorrelation.This preliminary study is a part of a larger project which aims to develop a decisionsupport tool to determine the deadline weeding intervention in a wheat crop taking intoaccount the weed harmfulness on wheat crop through a biomass prediction.Results are encouraging for next studies of this project. Nevertheless, weed detectionand biomass prediction can be increased looking at individually for each weed species.