With a focus toward mainly allowing mobile robot navigation, this work presents two methods for crop recognition and growth stage determination. These techniques comprise a two-phase approach with distinct models for crops and growth stage classification and a one-phase approach with a single model able of managing all crops and development phases. Using maize and sugar beet field images, the techniques were validated and shown to be both successful. For situations with a limited range of crops, the one-phase approach proved to be helpful since it allowed, with a single model, to identify both the type and growth state of the crop and shown an overall Mean Average Precision (mAP) of roughly 67.50%. Furthermore, the two-phase approach identified the crop type first, obtaining an overall mAP of roughly 74.2%, with maize detection showing quite good performance at 77.6%. Nevertheless, the mAP was only able to reach 61.3% when it came to determining the particular maize growth state since some challenges emerged when precisely classifying maize growth stages with six and eight leaves. Conversely, the two-phase approach is shown to be more scalable and flexible, hence it is a better option for systems allowing a variety of crops.