A novel automated cloud-based image datasets for high throughput phenotyping in weed classification.

Deep learning-based weed detection data management involves data acquisition, data labeling, model development, and model evaluation phases. Out of these data management phases, data acquisition and data labeling are labor-intensive and time-consuming steps for building robust models. In addition, low temporal variation of crop and weed in the datasets is one of the limiting factors for effective weed detection model development.