To evaluate the impact of weeds on crops, precise identification and early prediction are required. This paper presents two new non-destructive indicators deduced from visible images: weed pressure (WP) and wheat growth status (WGS). They are based on the fractional vegetation cover (FVC) obtained from digital vegetation maps (crop vs. weeds) in a wheat field. FVC was determined for both plants with a Matthews Correlation Coefficient of 0.86 using machine learning classification [support vector machine-radial basis function (SVM-RBF)] combined with Bag of Visual Words technique. It was compared to destructive measurements of above-ground biomass (BM) and leaf area index (LAI). Since the coefficient of determination between FVC and BM is very good for wheat crop (r2 = 0.93), FVC is used to feed a growth model based on the Monteith equation. Replacing the standard approach by the image approach in the initialization of the model had no impact on the simulated BM values. WP characterized weed pressure, namely the FVCw/FVCc ratio and it quantified the crop–weed competition. The results show that up to the tillering stage, it could substitute for the BMw/BMc ratio resulting from a destructive approach. The second indicator, WGS assessed crop health through the monitoring of biomass production. It compared the theoretical wheat biomass simulated under non-stressed conditions, BMsimulated, to the actual biomass, BMobserved. The impact of weed on crop was evaluated by combining the results of these two indicators. This simple and fast method based on proximal detection data offers promising results in agroecological cropping systems, where high responsiveness is a major challenge for site-specific weed management.