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Alfalfa (Medicago sativa L.) is used as a high-nutrient feed for animals. Weeds are a significant challenge that affects alfalfa production. Although weeds are unevenly distributed, herbicides are broadcast-applied in alfalfa fields. In this research, object detection convolutional neural networks, including Faster R-CNN, VarifocalNet (VFNet), and You Only Look Once Version 3 (YOLOv3), were used to indiscriminately detect all weed species (1-class) and discriminately detect between broadleaves and grasses (2-class). YOLOv3 outperformed other object detection networks in detecting grass weeds. The performances of using image classification networks (GoogLeNet and VGGNet) and object detection networks (Faster R-CNN and YOLOv3) for detecting broadleaves and grasses were compared. GoogLeNet and VGGNet (F1 scores ≥ 0.98) outperformed Faster R-CNN and YOLOv3 (F1 scores ≤ 0.92). Classifying and training various broadleaf and grass weeds did not improve the performance of the neural networks for weed detection. VGGNet was the most effective neural network (F1 scores ≥ 0.99) tested to detect broadleaf and grass weeds growing in alfalfa. Future research will integrate the VGGNet into the machine vision subsystem of smart sprayers for site-specific herbicide applications.