This work investigates how synthetic weather augmentation affects object detection training in precision agriculture and whether automatically generated plant masks can be used to remove invalid annotations and guide and improve artificial neural networks (NNs) training. To meet growing food demand, precision agriculture offers a promising solution by using image-based object detection with the help of NNs. NNs can locate plants in images by placing a bounding box (BB) around each plant and identifying it. This requires human-annotated data, which needs humans to manually define the BBs around each visible plant in the images. Artificial NNs adapt to the images they were trained on, meaning they perform best on images with conditions similar to their training data. In the real world images are influenced by various weather and environmental conditions that can affect detection performance. Therefore, it is necessary to train the model on images that represent a variety of weather conditions. We augment the images using computer vision techniques to make them appear as if they were taken under different weather conditions. After augmentation, not all plants remain visible, but re-annotating all the images would be extremely time-consuming. If we simply feed the augmented images with the original annotations into the training process, it hinders the training performance. Because plants are usually green, they can often be distinguished from the brown soil background. By using this color difference, we create a mask that separates the plants from the surrounding background. This mask is then used to determine whether a plant is still visible after augmentation by computing a threshold within its BB. We showed that more than 10% of the plants that were visible before augmentation were no longer visible afterwards. If a plant is no longer visible, its corresponding annotation is removed. These masks can also help guide NNs to focus on regions where plants are known to be present. By restricting the network to these regions instead of processing the entire image, computational cost and training time can be reduced while potentially improving detection performance. In a weed and crop image dataset, we showed that nearly 25% of each image could be excluded because these areas did not contain any plants. Future work will investigate if the mask-guided training improves the detection accuracy for different crops and weed datasets.