Automated approaches for the early stage distinguishing of Palmer amaranth from waterhemp

Palmer amaranth and waterhemp are two invasive pigweed species, which have become most troublesome to crops, especially corn and soybean. Among these two weed species, Palmer amaranth is more harmful to crops as it can grow faster, spread rapidly, and reduce crop yields significantly when compared to waterhemp. Distinguishing Palmer amaranth from waterhemp is important for effective weed management and an increase in crop production. However, differentiating these two weeds in the early stage is considerably difficult owing to their similar morphological characteristics. In the current study, three artificial intelligence approaches, namely machine learning (ML), deep learning (DL), and object detection (OD) were employed to automate the identification of greenhouse-grown Palmer amaranth and waterhemp within two weeks after emergence, from their RGB images. Aspect ratio, roundness, and circularity were measured and supplied as the input for the ML classification models. Among the four ML models employed, the random forest model achieved the top classification accuracy of 70% with only 312 training instances. In the case of deep learning, the proposed convolutional neural network model trained on a single-object RGB image of Palmer amaranth and waterhemp achieved a classification accuracy of 93%, outperforming the top ML model. The image dataset used for the DL model increased from the original size of 2,000 to 16,000 by various augmentation techniques. Finally, a transfer-learning-based object detection model for localized identification of the weeds was designed. The OD model was developed by fine-tuning the head of YOLOv5 trained on the COCO dataset with 3,200 single-object images (images with single foliage of either Palmer amaranth or waterhemp). The OD model developed in this study achieved an accuracy of 83.5% and it can identify the weed foliages irrespective of their size and proximity to each other.