  Deep learning, particularly Convolutional Neural Networks (CNNs), has gained
significant attention for its effectiveness in computer vision, especially in
agricultural tasks. Recent advancements in instance segmentation have improved
image classification accuracy. In this work, we introduce a comprehensive
dataset for training neural networks to detect weeds and soy plants through
instance segmentation. Our dataset covers various stages of soy growth,
offering a chronological perspective on weed invasion's impact, with 1,000
meticulously annotated images. We also provide 6 state of the art models,
trained in this dataset, that can understand and detect soy and weed in every
stage of the plantation process. By using this dataset for weed and soy
segmentation, we achieved a segmentation average precision of 79.1% and an
average recall of 69.2% across all plant classes, with the YOLOv8X model.
Moreover, the YOLOv8M model attained 78.7% mean average precision (mAp-50) in
caruru weed segmentation, 69.7% in grassy weed segmentation, and 90.1% in soy
plant segmentation.
