Deep Learning Yolo Based Weed Detection

In modern days, weed identification/plant detection in plants is more difficult. There has been little work so far to identify weeds while planting vegetables. Traditional approaches for the identification of agricultural weed were primarily directed at directly identifying weed; nevertheless, the variations in weed species are significant. This study presents a novel technique, which merges deep learning with imaging technology, as opposed to this method. First, the YOLO v3 model was used to train the dataset of the crop images along with the weed. The dataset consists of the images along with the Labeled data of the weed. Once after the completion of training, we can test the weed that was shown by the bounding boxes around the weed part.

Keywords: Weed detection, deep learning, and YOLO v3.