From Seedling to Harvest : The GrowingSoy Dataset for Weed Detection in Soy Crops via Instance Segmentation

Samenvatting

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 annotated images. To validate our data, 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, the best results achieved were a segmentation average precision of 79.1% and an average recall of 73.3% across all plant classes. Moreover, the YOLOv8M model attained 78.7% mean average precision (mAp-50) in caruru weed segmentation, 69.6% in grassy weed segmentation, and 90.1% in soy plant segmentation.